Overview

Brought to you by YData

Dataset statistics

Number of variables57
Number of observations1217482
Missing cells1791073
Missing cells (%)2.6%
Duplicate rows1477
Duplicate rows (%)0.1%
Total size in memory3.6 GiB
Average record size in memory3.1 KiB

Variable types

Numeric16
Text14
Categorical27

Alerts

ESTU_NIVEL_PRGM_ACADEMICO has constant value "UNIVERSITARIO" Constant
ESTU_ESTUDIANTE has constant value "ESTUDIANTE" Constant
Dataset has 1477 (0.1%) duplicate rowsDuplicates
ESTU_COD_COLE_MCPIO_TERMINO is highly overall correlated with ESTU_COD_DEPTO_PRESENTACION and 7 other fieldsHigh correlation
ESTU_COD_DEPTO_PRESENTACION is highly overall correlated with ESTU_COD_COLE_MCPIO_TERMINO and 6 other fieldsHigh correlation
ESTU_COD_MCPIO_PRESENTACION is highly overall correlated with ESTU_COD_COLE_MCPIO_TERMINO and 6 other fieldsHigh correlation
ESTU_COD_RESIDE_DEPTO is highly overall correlated with ESTU_COD_COLE_MCPIO_TERMINO and 5 other fieldsHigh correlation
ESTU_COD_RESIDE_MCPIO is highly overall correlated with ESTU_COD_COLE_MCPIO_TERMINO and 7 other fieldsHigh correlation
ESTU_INST_CODMUNICIPIO is highly overall correlated with ESTU_COD_COLE_MCPIO_TERMINO and 7 other fieldsHigh correlation
ESTU_INST_DEPARTAMENTO is highly overall correlated with ESTU_COD_COLE_MCPIO_TERMINO and 4 other fieldsHigh correlation
ESTU_PRGM_CODMUNICIPIO is highly overall correlated with ESTU_COD_COLE_MCPIO_TERMINO and 7 other fieldsHigh correlation
ESTU_PRGM_DEPARTAMENTO is highly overall correlated with ESTU_COD_COLE_MCPIO_TERMINO and 6 other fieldsHigh correlation
INST_CARACTER_ACADEMICO is highly overall correlated with INST_COD_INSTITUCIONHigh correlation
INST_COD_INSTITUCION is highly overall correlated with INST_CARACTER_ACADEMICOHigh correlation
MOD_COMPETEN_CIUDADA_PUNT is highly overall correlated with MOD_INGLES_PUNT and 2 other fieldsHigh correlation
MOD_COMUNI_ESCRITA_DESEM is highly overall correlated with MOD_COMUNI_ESCRITA_PUNTHigh correlation
MOD_COMUNI_ESCRITA_PUNT is highly overall correlated with MOD_COMUNI_ESCRITA_DESEMHigh correlation
MOD_INGLES_DESEM is highly overall correlated with MOD_INGLES_PUNTHigh correlation
MOD_INGLES_PUNT is highly overall correlated with MOD_COMPETEN_CIUDADA_PUNT and 3 other fieldsHigh correlation
MOD_LECTURA_CRITICA_PUNT is highly overall correlated with MOD_COMPETEN_CIUDADA_PUNT and 2 other fieldsHigh correlation
MOD_RAZONA_CUANTITAT_PUNT is highly overall correlated with MOD_COMPETEN_CIUDADA_PUNT and 2 other fieldsHigh correlation
ESTU_TIPODOCUMENTO is highly imbalanced (98.4%) Imbalance
ESTU_PRIVADO_LIBERTAD is highly imbalanced (99.9%) Imbalance
ESTU_ESTADOINVESTIGACION is highly imbalanced (98.9%) Imbalance
ESTU_TIPODOCUMENTOSB11 is highly imbalanced (61.5%) Imbalance
FAMI_TIENECOMPUTADOR is highly imbalanced (55.5%) Imbalance
ESTU_CODDANE_COLE_TERMINO has 400042 (32.9%) missing values Missing
ESTU_COD_COLE_MCPIO_TERMINO has 400042 (32.9%) missing values Missing
ESTU_PAGOMATRICULABECA has 12550 (1.0%) missing values Missing
ESTU_PAGOMATRICULACREDITO has 12470 (1.0%) missing values Missing
ESTU_HORASSEMANATRABAJA has 55467 (4.6%) missing values Missing
ESTU_COLE_TERMINO has 375648 (30.9%) missing values Missing
ESTU_PAGOMATRICULAPADRES has 12352 (1.0%) missing values Missing
ESTU_PAGOMATRICULAPROPIO has 12416 (1.0%) missing values Missing
ESTU_TIPODOCUMENTOSB11 has 26035 (2.1%) missing values Missing
FAMI_EDUCACIONPADRE has 41222 (3.4%) missing values Missing
FAMI_TIENEAUTOMOVIL has 76214 (6.3%) missing values Missing
FAMI_TIENELAVADORA has 67718 (5.6%) missing values Missing
FAMI_ESTRATOVIVIENDA has 55103 (4.5%) missing values Missing
FAMI_TIENECOMPUTADOR has 64913 (5.3%) missing values Missing
FAMI_TIENEINTERNET has 47504 (3.9%) missing values Missing
FAMI_EDUCACIONMADRE has 42018 (3.5%) missing values Missing
MOD_COMUNI_ESCRITA_DESEM has 55472 (4.6%) missing values Missing
ESTU_COD_RESIDE_DEPTO is highly skewed (γ1 = 51.27588772) Skewed
MOD_COMUNI_ESCRITA_PUNT has 48006 (3.9%) zeros Zeros

Reproduction

Analysis started2025-05-21 12:09:33.586392
Analysis finished2025-05-21 12:28:39.279594
Duration19 minutes and 5.69 seconds
Software versionydata-profiling vv4.16.1
Download configurationconfig.json

Variables

PERIODO
Real number (ℝ)

Distinct13
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20203.096
Minimum20183
Maximum20226
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size9.3 MiB
2025-05-21T17:28:39.359761image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum20183
5-th percentile20183
Q120195
median20203
Q320212
95-th percentile20225
Maximum20226
Range43
Interquartile range (IQR)17

Descriptive statistics

Standard deviation13.696877
Coefficient of variation (CV)0.00067795931
Kurtosis-1.061229
Mean20203.096
Median Absolute Deviation (MAD)9
Skewness0.042315598
Sum2.4596905 × 1010
Variance187.60444
MonotonicityNot monotonic
2025-05-21T17:28:39.563391image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
20195 258452
21.2%
20212 247099
20.3%
20203 245775
20.2%
20183 235224
19.3%
20225 126282
10.4%
20222 97889
 
8.0%
20194 2076
 
0.2%
20213 1670
 
0.1%
20226 1060
 
0.1%
20202 680
 
0.1%
Other values (3) 1275
 
0.1%
ValueCountFrequency (%)
20183 235224
19.3%
20184 373
 
< 0.1%
20194 2076
 
0.2%
20195 258452
21.2%
20196 228
 
< 0.1%
20202 680
 
0.1%
20203 245775
20.2%
20212 247099
20.3%
20213 1670
 
0.1%
20222 97889
 
8.0%
ValueCountFrequency (%)
20226 1060
 
0.1%
20225 126282
10.4%
20223 674
 
0.1%
20222 97889
 
8.0%
20213 1670
 
0.1%
20212 247099
20.3%
20203 245775
20.2%
20202 680
 
0.1%
20196 228
 
< 0.1%
20195 258452
21.2%
Distinct1212456
Distinct (%)99.6%
Missing0
Missing (%)0.0%
Memory size82.4 MiB
2025-05-21T17:28:41.105903image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length14
Median length14
Mean length14
Min length14

Characters and Unicode

Total characters17044748
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1207430 ?
Unique (%)99.2%

Sample

1st rowEK201830011083
2nd rowEK201830053875
3rd rowEK201830167993
4th rowEK201830168158
5th rowEK201830164354
ValueCountFrequency (%)
ek202120076186 2
 
< 0.1%
ek202120070361 2
 
< 0.1%
ek202120126755 2
 
< 0.1%
ek202120287324 2
 
< 0.1%
ek202120075079 2
 
< 0.1%
ek202120122684 2
 
< 0.1%
ek202250195536 2
 
< 0.1%
ek202120107126 2
 
< 0.1%
ek202120152714 2
 
< 0.1%
ek202250183095 2
 
< 0.1%
Other values (1212446) 1217462
> 99.9%
2025-05-21T17:28:42.661703image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 3502192
20.5%
2 3471977
20.4%
1 1937439
11.4%
K 1217482
 
7.1%
E 1217482
 
7.1%
3 1199871
 
7.0%
5 975523
 
5.7%
9 879819
 
5.2%
8 844778
 
5.0%
7 603677
 
3.5%
Other values (2) 1194508
 
7.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 17044748
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 3502192
20.5%
2 3471977
20.4%
1 1937439
11.4%
K 1217482
 
7.1%
E 1217482
 
7.1%
3 1199871
 
7.0%
5 975523
 
5.7%
9 879819
 
5.2%
8 844778
 
5.0%
7 603677
 
3.5%
Other values (2) 1194508
 
7.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 17044748
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 3502192
20.5%
2 3471977
20.4%
1 1937439
11.4%
K 1217482
 
7.1%
E 1217482
 
7.1%
3 1199871
 
7.0%
5 975523
 
5.7%
9 879819
 
5.2%
8 844778
 
5.0%
7 603677
 
3.5%
Other values (2) 1194508
 
7.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 17044748
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 3502192
20.5%
2 3471977
20.4%
1 1937439
11.4%
K 1217482
 
7.1%
E 1217482
 
7.1%
3 1199871
 
7.0%
5 975523
 
5.7%
9 879819
 
5.2%
8 844778
 
5.0%
7 603677
 
3.5%
Other values (2) 1194508
 
7.0%

ESTU_TIPODOCUMENTO
Categorical

Imbalance 

Distinct9
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size68.5 MiB
CC
1211795 
TI
 
2545
CE
 
1891
CR
 
723
PE
 
376
Other values (4)
 
152

Length

Max length3
Median length2
Mean length2.0000049
Min length1

Characters and Unicode

Total characters2434970
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowCC
2nd rowCC
3rd rowCC
4th rowCC
5th rowCC

Common Values

ValueCountFrequency (%)
CC 1211795
99.5%
TI 2545
 
0.2%
CE 1891
 
0.2%
CR 723
 
0.1%
PE 376
 
< 0.1%
PC 136
 
< 0.1%
PEP 10
 
< 0.1%
V 5
 
< 0.1%
PPT 1
 
< 0.1%

Length

2025-05-21T17:28:42.968274image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-21T17:28:43.276004image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
cc 1211795
99.5%
ti 2545
 
0.2%
ce 1891
 
0.2%
cr 723
 
0.1%
pe 376
 
< 0.1%
pc 136
 
< 0.1%
pep 10
 
< 0.1%
v 5
 
< 0.1%
ppt 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
C 2426340
99.6%
T 2546
 
0.1%
I 2545
 
0.1%
E 2277
 
0.1%
R 723
 
< 0.1%
P 534
 
< 0.1%
V 5
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2434970
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
C 2426340
99.6%
T 2546
 
0.1%
I 2545
 
0.1%
E 2277
 
0.1%
R 723
 
< 0.1%
P 534
 
< 0.1%
V 5
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2434970
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
C 2426340
99.6%
T 2546
 
0.1%
I 2545
 
0.1%
E 2277
 
0.1%
R 723
 
< 0.1%
P 534
 
< 0.1%
V 5
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2434970
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
C 2426340
99.6%
T 2546
 
0.1%
I 2545
 
0.1%
E 2277
 
0.1%
R 723
 
< 0.1%
P 534
 
< 0.1%
V 5
 
< 0.1%
Distinct86
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size75.5 MiB
2025-05-21T17:28:44.237569image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length40
Median length8
Mean length8.0003926
Min length4

Characters and Unicode

Total characters9740334
Distinct characters33
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique22 ?
Unique (%)< 0.1%

Sample

1st rowCOLOMBIA
2nd rowCOLOMBIA
3rd rowCOLOMBIA
4th rowCOLOMBIA
5th rowCOLOMBIA
ValueCountFrequency (%)
colombia 1215091
99.8%
venezuela 885
 
0.1%
perú 182
 
< 0.1%
ecuador 170
 
< 0.1%
francia 138
 
< 0.1%
estados 113
 
< 0.1%
unidos 111
 
< 0.1%
panamá 86
 
< 0.1%
méxico 71
 
< 0.1%
alemania 67
 
< 0.1%
Other values (99) 903
 
0.1%
2025-05-21T17:28:44.775969image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
O 2430962
25.0%
A 1217971
12.5%
L 1216383
12.5%
I 1216017
12.5%
C 1215777
12.5%
M 1215406
12.5%
B 1215266
12.5%
E 3574
 
< 0.1%
N 1585
 
< 0.1%
U 1355
 
< 0.1%
Other values (23) 6038
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 9740334
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
O 2430962
25.0%
A 1217971
12.5%
L 1216383
12.5%
I 1216017
12.5%
C 1215777
12.5%
M 1215406
12.5%
B 1215266
12.5%
E 3574
 
< 0.1%
N 1585
 
< 0.1%
U 1355
 
< 0.1%
Other values (23) 6038
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 9740334
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
O 2430962
25.0%
A 1217971
12.5%
L 1216383
12.5%
I 1216017
12.5%
C 1215777
12.5%
M 1215406
12.5%
B 1215266
12.5%
E 3574
 
< 0.1%
N 1585
 
< 0.1%
U 1355
 
< 0.1%
Other values (23) 6038
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 9740334
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
O 2430962
25.0%
A 1217971
12.5%
L 1216383
12.5%
I 1216017
12.5%
C 1215777
12.5%
M 1215406
12.5%
B 1215266
12.5%
E 3574
 
< 0.1%
N 1585
 
< 0.1%
U 1355
 
< 0.1%
Other values (23) 6038
 
0.1%

ESTU_COD_RESIDE_DEPTO
Real number (ℝ)

High correlation  Skewed 

Distinct35
Distinct (%)< 0.1%
Missing3393
Missing (%)0.3%
Infinite0
Infinite (%)0.0%
Mean67.839896
Minimum0
Maximum99999
Zeros20
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size9.3 MiB
2025-05-21T17:28:44.959082image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile5
Q111
median15
Q352
95-th percentile76
Maximum99999
Range99999
Interquartile range (IQR)41

Descriptive statistics

Standard deviation1947.8117
Coefficient of variation (CV)28.711891
Kurtosis2627.6841
Mean67.839896
Median Absolute Deviation (MAD)10
Skewness51.275888
Sum82363672
Variance3793970.6
MonotonicityNot monotonic
2025-05-21T17:28:45.209123image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=35)
ValueCountFrequency (%)
11 326784
26.8%
5 146234
12.0%
76 95745
 
7.9%
25 76074
 
6.2%
8 69830
 
5.7%
68 58419
 
4.8%
13 39027
 
3.2%
54 36487
 
3.0%
15 32657
 
2.7%
73 31600
 
2.6%
Other values (25) 301232
24.7%
ValueCountFrequency (%)
0 20
 
< 0.1%
5 146234
12.0%
8 69830
 
5.7%
11 326784
26.8%
13 39027
 
3.2%
15 32657
 
2.7%
17 23130
 
1.9%
18 6881
 
0.6%
19 23465
 
1.9%
20 21919
 
1.8%
ValueCountFrequency (%)
99999 461
 
< 0.1%
99 351
 
< 0.1%
97 243
 
< 0.1%
95 963
 
0.1%
94 184
 
< 0.1%
91 356
 
< 0.1%
88 922
 
0.1%
86 4926
0.4%
85 7786
0.6%
81 3538
0.3%
Distinct51
Distinct (%)< 0.1%
Missing3393
Missing (%)0.3%
Memory size85.6 MiB
2025-05-21T17:28:45.653054image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length17
Median length15
Mean length7.378282
Min length4

Characters and Unicode

Total characters8957891
Distinct characters28
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique14 ?
Unique (%)< 0.1%

Sample

1st rowBOGOTÁ
2nd rowVALLE
3rd rowTOLIMA
4th rowSANTANDER
5th rowBOGOTÁ
ValueCountFrequency (%)
bogotá 326784
25.8%
antioquia 146234
11.6%
valle 95745
 
7.6%
santander 94906
 
7.5%
cundinamarca 76074
 
6.0%
atlantico 69830
 
5.5%
bolivar 39027
 
3.1%
norte 36487
 
2.9%
boyaca 32657
 
2.6%
tolima 31600
 
2.5%
Other values (48) 314843
24.9%
2025-05-21T17:28:46.203290image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
A 1537637
17.2%
O 1135194
12.7%
T 812283
 
9.1%
N 669004
 
7.5%
I 632793
 
7.1%
L 438634
 
4.9%
C 427935
 
4.8%
B 427431
 
4.8%
R 420352
 
4.7%
G 360135
 
4.0%
Other values (18) 2096493
23.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 8957891
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A 1537637
17.2%
O 1135194
12.7%
T 812283
 
9.1%
N 669004
 
7.5%
I 632793
 
7.1%
L 438634
 
4.9%
C 427935
 
4.8%
B 427431
 
4.8%
R 420352
 
4.7%
G 360135
 
4.0%
Other values (18) 2096493
23.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 8957891
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A 1537637
17.2%
O 1135194
12.7%
T 812283
 
9.1%
N 669004
 
7.5%
I 632793
 
7.1%
L 438634
 
4.9%
C 427935
 
4.8%
B 427431
 
4.8%
R 420352
 
4.7%
G 360135
 
4.0%
Other values (18) 2096493
23.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 8957891
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A 1537637
17.2%
O 1135194
12.7%
T 812283
 
9.1%
N 669004
 
7.5%
I 632793
 
7.1%
L 438634
 
4.9%
C 427935
 
4.8%
B 427431
 
4.8%
R 420352
 
4.7%
G 360135
 
4.0%
Other values (18) 2096493
23.4%

ESTU_COD_RESIDE_MCPIO
Real number (ℝ)

High correlation 

Distinct1129
Distinct (%)0.1%
Missing3393
Missing (%)0.3%
Infinite0
Infinite (%)0.0%
Mean30051.295
Minimum1
Maximum99999
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size9.3 MiB
2025-05-21T17:28:46.358931image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile5001
Q111001
median15632
Q352356
95-th percentile76001
Maximum99999
Range99998
Interquartile range (IQR)41355

Descriptive statistics

Standard deviation25898.296
Coefficient of variation (CV)0.86180299
Kurtosis-0.95289249
Mean30051.295
Median Absolute Deviation (MAD)9754
Skewness0.80460239
Sum3.6484946 × 1010
Variance6.7072172 × 108
MonotonicityNot monotonic
2025-05-21T17:28:46.641959image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11001 326784
26.8%
5001 76695
 
6.3%
76001 58192
 
4.8%
8001 46302
 
3.8%
13001 30410
 
2.5%
68001 24816
 
2.0%
73001 21783
 
1.8%
54001 20734
 
1.7%
50001 18855
 
1.5%
52001 18474
 
1.5%
Other values (1119) 571044
46.9%
ValueCountFrequency (%)
1 2
< 0.1%
3 1
< 0.1%
6 1
< 0.1%
9 1
< 0.1%
17 1
< 0.1%
18 1
< 0.1%
19 2
< 0.1%
21 1
< 0.1%
22 1
< 0.1%
23 1
< 0.1%
ValueCountFrequency (%)
99999 461
< 0.1%
99773 37
 
< 0.1%
99624 26
 
< 0.1%
99572 1
 
< 0.1%
99524 47
 
< 0.1%
99001 240
< 0.1%
97889 1
 
< 0.1%
97666 3
 
< 0.1%
97161 4
 
< 0.1%
97001 235
< 0.1%
Distinct1044
Distinct (%)0.1%
Missing3393
Missing (%)0.3%
Memory size97.2 MiB
2025-05-21T17:28:47.228183image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length27
Median length25
Mean length9.2331584
Min length3

Characters and Unicode

Total characters11209876
Distinct characters36
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique15 ?
Unique (%)< 0.1%

Sample

1st rowBOGOTÁ D.C.
2nd rowSEVILLA
3rd rowIBAGUÉ
4th rowBARRANCABERMEJA
5th rowBOGOTÁ D.C.
ValueCountFrequency (%)
d.c 326784
18.9%
bogotá 326784
18.9%
medellín 76695
 
4.4%
cali 58192
 
3.4%
de 52523
 
3.0%
barranquilla 46302
 
2.7%
cartagena 30496
 
1.8%
indias 30410
 
1.8%
bucaramanga 24816
 
1.4%
ibagué 21783
 
1.3%
Other values (1034) 734421
42.5%
2025-05-21T17:28:47.896798image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
A 1386011
 
12.4%
O 1001164
 
8.9%
C 713389
 
6.4%
. 653568
 
5.8%
D 637986
 
5.7%
L 618275
 
5.5%
E 585943
 
5.2%
T 582947
 
5.2%
I 538576
 
4.8%
B 528048
 
4.7%
Other values (26) 3963969
35.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 11209876
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A 1386011
 
12.4%
O 1001164
 
8.9%
C 713389
 
6.4%
. 653568
 
5.8%
D 637986
 
5.7%
L 618275
 
5.5%
E 585943
 
5.2%
T 582947
 
5.2%
I 538576
 
4.8%
B 528048
 
4.7%
Other values (26) 3963969
35.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 11209876
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A 1386011
 
12.4%
O 1001164
 
8.9%
C 713389
 
6.4%
. 653568
 
5.8%
D 637986
 
5.7%
L 618275
 
5.5%
E 585943
 
5.2%
T 582947
 
5.2%
I 538576
 
4.8%
B 528048
 
4.7%
Other values (26) 3963969
35.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 11209876
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A 1386011
 
12.4%
O 1001164
 
8.9%
C 713389
 
6.4%
. 653568
 
5.8%
D 637986
 
5.7%
L 618275
 
5.5%
E 585943
 
5.2%
T 582947
 
5.2%
I 538576
 
4.8%
B 528048
 
4.7%
Other values (26) 3963969
35.4%

ESTU_CODDANE_COLE_TERMINO
Real number (ℝ)

Missing 

Distinct12623
Distinct (%)1.5%
Missing400042
Missing (%)32.9%
Infinite0
Infinite (%)0.0%
Mean2.160293 × 1011
Minimum0
Maximum8.230011 × 1011
Zeros70
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size9.3 MiB
2025-05-21T17:28:48.108980image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1.05318 × 1011
Q11.19573 × 1011
median1.73043 × 1011
Q33.1100111 × 1011
95-th percentile3.76001 × 1011
Maximum8.230011 × 1011
Range8.230011 × 1011
Interquartile range (IQR)1.9142811 × 1011

Descriptive statistics

Standard deviation9.9659527 × 1010
Coefficient of variation (CV)0.46132412
Kurtosis-1.2490376
Mean2.160293 × 1011
Median Absolute Deviation (MAD)6.4285 × 1010
Skewness0.41933158
Sum1.7659099 × 1017
Variance9.9320213 × 1021
MonotonicityNot monotonic
2025-05-21T17:28:48.378965image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.050010001 × 10113080
 
0.3%
1.050010133 × 10112144
 
0.2%
3.050010043 × 10111697
 
0.1%
1.110010247 × 10111634
 
0.1%
3.05001004 × 10111435
 
0.1%
3.130010031 × 10111243
 
0.1%
1.110010194 × 10111238
 
0.1%
1.110010118 × 10111218
 
0.1%
1.520010008 × 10111209
 
0.1%
1.50001001 × 10111198
 
0.1%
Other values (12613) 801344
65.8%
(Missing) 400042
32.9%
ValueCountFrequency (%)
0 70
 
< 0.1%
900086079 5
 
< 0.1%
1.050010339 × 1010119
 
< 0.1%
1.230010534 × 10107
 
< 0.1%
1.500010187 × 1010422
< 0.1%
1.733490003 × 101030
 
< 0.1%
3.00800141 × 10107
 
< 0.1%
3.080010096 × 101022
 
< 0.1%
3.080010184 × 101037
 
< 0.1%
3.080011014 × 101035
 
< 0.1%
ValueCountFrequency (%)
8.230011 × 101114
 
< 0.1%
8.180011 × 101120
 
< 0.1%
8.1343 × 101110
 
< 0.1%
7.108001 × 10113
 
< 0.1%
6.258430001 × 10113
 
< 0.1%
6.254730001 × 10113
 
< 0.1%
5.684320013 × 101116
 
< 0.1%
5.680010097 × 101119
 
< 0.1%
5.680010092 × 101111
 
< 0.1%
5.540013 × 1011113
< 0.1%

ESTU_COD_COLE_MCPIO_TERMINO
Real number (ℝ)

High correlation  Missing 

Distinct1106
Distinct (%)0.1%
Missing400042
Missing (%)32.9%
Infinite0
Infinite (%)0.0%
Mean33106.664
Minimum5001
Maximum99773
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size9.3 MiB
2025-05-21T17:28:48.555997image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum5001
5-th percentile5001
Q111001
median20400
Q354498
95-th percentile76111
Maximum99773
Range94772
Interquartile range (IQR)43497

Descriptive statistics

Standard deviation26390.745
Coefficient of variation (CV)0.79714298
Kurtosis-1.2225216
Mean33106.664
Median Absolute Deviation (MAD)14730
Skewness0.59362476
Sum2.7062711 × 1010
Variance6.964714 × 108
MonotonicityNot monotonic
2025-05-21T17:28:48.758413image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11001 160524
 
13.2%
5001 44742
 
3.7%
76001 35872
 
2.9%
8001 27077
 
2.2%
13001 19863
 
1.6%
73001 15537
 
1.3%
54001 14785
 
1.2%
68001 14365
 
1.2%
50001 13405
 
1.1%
52001 11997
 
1.0%
Other values (1096) 459273
37.7%
(Missing) 400042
32.9%
ValueCountFrequency (%)
5001 44742
3.7%
5002 225
 
< 0.1%
5004 18
 
< 0.1%
5021 12
 
< 0.1%
5030 272
 
< 0.1%
5031 208
 
< 0.1%
5034 513
 
< 0.1%
5036 33
 
< 0.1%
5038 48
 
< 0.1%
5040 55
 
< 0.1%
ValueCountFrequency (%)
99773 56
 
< 0.1%
99624 27
 
< 0.1%
99524 130
 
< 0.1%
99001 294
 
< 0.1%
97889 2
 
< 0.1%
97001 247
 
< 0.1%
95200 10
 
< 0.1%
95025 40
 
< 0.1%
95015 34
 
< 0.1%
95001 864
0.1%

ESTU_COD_DEPTO_PRESENTACION
Real number (ℝ)

High correlation 

Distinct34
Distinct (%)< 0.1%
Missing104
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean21.114056
Minimum0
Maximum99
Zeros2078
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size9.3 MiB
2025-05-21T17:28:48.975257image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile5
Q111
median11
Q319
95-th percentile73
Maximum99
Range99
Interquartile range (IQR)8

Descriptive statistics

Standard deviation21.070154
Coefficient of variation (CV)0.99792075
Kurtosis1.5821298
Mean21.114056
Median Absolute Deviation (MAD)0
Skewness1.7618152
Sum25703787
Variance443.95141
MonotonicityNot monotonic
2025-05-21T17:28:49.179757image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=34)
ValueCountFrequency (%)
11 737228
60.6%
5 73041
 
6.0%
76 49682
 
4.1%
8 42522
 
3.5%
25 33113
 
2.7%
68 31569
 
2.6%
54 22671
 
1.9%
13 21998
 
1.8%
23 17748
 
1.5%
15 16956
 
1.4%
Other values (24) 170850
 
14.0%
ValueCountFrequency (%)
0 2078
 
0.2%
5 73041
 
6.0%
8 42522
 
3.5%
11 737228
60.6%
13 21998
 
1.8%
15 16956
 
1.4%
17 13219
 
1.1%
18 4247
 
0.3%
19 13905
 
1.1%
20 12851
 
1.1%
ValueCountFrequency (%)
99 158
 
< 0.1%
97 177
 
< 0.1%
95 490
 
< 0.1%
94 119
 
< 0.1%
91 262
 
< 0.1%
88 447
 
< 0.1%
86 2454
 
0.2%
85 3456
 
0.3%
81 1452
 
0.1%
76 49682
4.1%

INST_COD_INSTITUCION
Real number (ℝ)

High correlation 

Distinct269
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2331.7091
Minimum1101
Maximum9932
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size9.3 MiB
2025-05-21T17:28:49.461644image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1101
5-th percentile1111
Q11701
median1825
Q32812
95-th percentile4813
Maximum9932
Range8831
Interquartile range (IQR)1111

Descriptive statistics

Standard deviation1426.1353
Coefficient of variation (CV)0.61162659
Kurtosis12.166401
Mean2331.7091
Median Absolute Deviation (MAD)705
Skewness3.0680941
Sum2.8388139 × 109
Variance2033861.9
MonotonicityNot monotonic
2025-05-21T17:28:49.708941image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2829 90169
 
7.4%
2725 49852
 
4.1%
2102 35613
 
2.9%
2728 28997
 
2.4%
1818 21971
 
1.8%
1212 17295
 
1.4%
1201 17108
 
1.4%
4813 16267
 
1.3%
1701 15417
 
1.3%
1101 15336
 
1.3%
Other values (259) 909457
74.7%
ValueCountFrequency (%)
1101 15336
1.3%
1102 5138
 
0.4%
1103 3120
 
0.3%
1104 1600
 
0.1%
1105 5612
 
0.5%
1106 10615
0.9%
1107 1634
 
0.1%
1108 1932
 
0.2%
1109 423
 
< 0.1%
1110 7284
0.6%
ValueCountFrequency (%)
9932 1
 
< 0.1%
9930 82
 
< 0.1%
9927 17
 
< 0.1%
9926 143
 
< 0.1%
9922 368
 
< 0.1%
9921 49
 
< 0.1%
9915 370
 
< 0.1%
9914 215
 
< 0.1%
9913 2645
0.2%
9907 1163
0.1%
Distinct272
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size161.7 MiB
2025-05-21T17:28:50.048998image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length96
Median length80
Mean length45.798112
Min length22

Characters and Unicode

Total characters55758377
Distinct characters52
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5 ?
Unique (%)< 0.1%

Sample

1st rowUNIVERSITARIA AGUSTINIANA- UNIAGUSTINIANA-BOGOTÁ D.C.
2nd rowUNIVERSIDAD DEL VALLE-CALI
3rd rowCORPORACION UNIVERSITARIA MINUTO DE DIOS -UNIMINUTO-BOGOTÁ D.C.
4th rowINSTITUTO UNIVERSITARIO DE LA PAZ-BARRANCABERMEJA
5th rowUNIVERSIDAD DEL QUINDIO-ARMENIA
ValueCountFrequency (%)
universidad 734376
 
12.8%
de 617015
 
10.7%
d.c 549832
 
9.5%
universitaria 319407
 
5.5%
corporacion 213837
 
3.7%
del 188468
 
3.3%
fundacion 143120
 
2.5%
94288
 
1.6%
dios 91799
 
1.6%
minuto 91799
 
1.6%
Other values (454) 2714550
47.1%
2025-05-21T17:28:50.777696image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
A 5951207
 
10.7%
I 5642432
 
10.1%
4570983
 
8.2%
O 4305685
 
7.7%
N 4070619
 
7.3%
D 3717931
 
6.7%
E 3582115
 
6.4%
R 3195744
 
5.7%
C 2787843
 
5.0%
U 2389386
 
4.3%
Other values (42) 15544432
27.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 55758377
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A 5951207
 
10.7%
I 5642432
 
10.1%
4570983
 
8.2%
O 4305685
 
7.7%
N 4070619
 
7.3%
D 3717931
 
6.7%
E 3582115
 
6.4%
R 3195744
 
5.7%
C 2787843
 
5.0%
U 2389386
 
4.3%
Other values (42) 15544432
27.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 55758377
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A 5951207
 
10.7%
I 5642432
 
10.1%
4570983
 
8.2%
O 4305685
 
7.7%
N 4070619
 
7.3%
D 3717931
 
6.7%
E 3582115
 
6.4%
R 3195744
 
5.7%
C 2787843
 
5.0%
U 2389386
 
4.3%
Other values (42) 15544432
27.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 55758377
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A 5951207
 
10.7%
I 5642432
 
10.1%
4570983
 
8.2%
O 4305685
 
7.7%
N 4070619
 
7.3%
D 3717931
 
6.7%
E 3582115
 
6.4%
R 3195744
 
5.7%
C 2787843
 
5.0%
U 2389386
 
4.3%
Other values (42) 15544432
27.9%

INST_CARACTER_ACADEMICO
Categorical

High correlation 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size108.5 MiB
UNIVERSIDAD
735155 
INSTITUCIÓN UNIVERSITARIA
436563 
TÉCNICA PROFESIONAL
 
24572
INSTITUCIÓN TECNOLÓGICA
 
21192

Length

Max length25
Median length11
Mean length16.390439
Min length11

Characters and Unicode

Total characters19955064
Distinct characters19
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowINSTITUCIÓN UNIVERSITARIA
2nd rowUNIVERSIDAD
3rd rowINSTITUCIÓN UNIVERSITARIA
4th rowINSTITUCIÓN UNIVERSITARIA
5th rowUNIVERSIDAD

Common Values

ValueCountFrequency (%)
UNIVERSIDAD 735155
60.4%
INSTITUCIÓN UNIVERSITARIA 436563
35.9%
TÉCNICA PROFESIONAL 24572
 
2.0%
INSTITUCIÓN TECNOLÓGICA 21192
 
1.7%

Length

2025-05-21T17:28:50.941855image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-21T17:28:51.144693image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
universidad 735155
43.2%
institución 457755
26.9%
universitaria 436563
25.7%
técnica 24572
 
1.4%
profesional 24572
 
1.4%
tecnológica 21192
 
1.2%

Most occurring characters

ValueCountFrequency (%)
I 4223600
21.2%
N 2157564
10.8%
A 1678617
 
8.4%
S 1654045
 
8.3%
R 1632853
 
8.2%
U 1629473
 
8.2%
D 1470310
 
7.4%
T 1397837
 
7.0%
E 1217482
 
6.1%
V 1171718
 
5.9%
Other values (9) 1721565
8.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 19955064
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
I 4223600
21.2%
N 2157564
10.8%
A 1678617
 
8.4%
S 1654045
 
8.3%
R 1632853
 
8.2%
U 1629473
 
8.2%
D 1470310
 
7.4%
T 1397837
 
7.0%
E 1217482
 
6.1%
V 1171718
 
5.9%
Other values (9) 1721565
8.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 19955064
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
I 4223600
21.2%
N 2157564
10.8%
A 1678617
 
8.4%
S 1654045
 
8.3%
R 1632853
 
8.2%
U 1629473
 
8.2%
D 1470310
 
7.4%
T 1397837
 
7.0%
E 1217482
 
6.1%
V 1171718
 
5.9%
Other values (9) 1721565
8.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 19955064
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
I 4223600
21.2%
N 2157564
10.8%
A 1678617
 
8.4%
S 1654045
 
8.3%
R 1632853
 
8.2%
U 1629473
 
8.2%
D 1470310
 
7.4%
T 1397837
 
7.0%
E 1217482
 
6.1%
V 1171718
 
5.9%
Other values (9) 1721565
8.6%
Distinct56
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size134.9 MiB
2025-05-21T17:28:51.558285image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length53
Median length45
Mean length19.855477
Min length6

Characters and Unicode

Total characters24173686
Distinct characters32
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowADMINISTRACIÓN
2nd rowCONTADURÍA PUBLICA
3rd rowCONTADURÍA PUBLICA
4th rowINGENIERÍA AMBIENTAL, SANITARIA Y AFINES
5th rowBIBLIOTECOLOGÍA, OTROS DE CIENCIAS SOCIALES Y HUMANAS
ValueCountFrequency (%)
y 463530
15.7%
afines 449904
15.2%
ingeniería 256020
 
8.7%
administración 251455
 
8.5%
educación 118605
 
4.0%
publica 99676
 
3.4%
derecho 97009
 
3.3%
contaduría 90022
 
3.1%
psicología 86856
 
2.9%
industrial 65767
 
2.2%
Other values (94) 971513
32.9%
2025-05-21T17:28:52.191420image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
I 3173677
13.1%
A 2797111
11.6%
N 2180690
 
9.0%
E 2037337
 
8.4%
1732875
 
7.2%
C 1560603
 
6.5%
S 1378456
 
5.7%
R 1184543
 
4.9%
O 994837
 
4.1%
T 910961
 
3.8%
Other values (22) 6222596
25.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 24173686
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
I 3173677
13.1%
A 2797111
11.6%
N 2180690
 
9.0%
E 2037337
 
8.4%
1732875
 
7.2%
C 1560603
 
6.5%
S 1378456
 
5.7%
R 1184543
 
4.9%
O 994837
 
4.1%
T 910961
 
3.8%
Other values (22) 6222596
25.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 24173686
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
I 3173677
13.1%
A 2797111
11.6%
N 2180690
 
9.0%
E 2037337
 
8.4%
1732875
 
7.2%
C 1560603
 
6.5%
S 1378456
 
5.7%
R 1184543
 
4.9%
O 994837
 
4.1%
T 910961
 
3.8%
Other values (22) 6222596
25.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 24173686
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
I 3173677
13.1%
A 2797111
11.6%
N 2180690
 
9.0%
E 2037337
 
8.4%
1732875
 
7.2%
C 1560603
 
6.5%
S 1378456
 
5.7%
R 1184543
 
4.9%
O 994837
 
4.1%
T 910961
 
3.8%
Other values (22) 6222596
25.7%

ESTU_INST_DEPARTAMENTO
Categorical

High correlation 

Distinct26
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size91.8 MiB
BOGOTÁ
549976 
ANTIOQUIA
142356 
ATLANTICO
76332 
VALLE
71065 
SANTANDER
 
52474
Other values (21)
325279 

Length

Max length15
Median length6
Mean length7.0634071
Min length4

Characters and Unicode

Total characters8599571
Distinct characters24
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowBOGOTÁ
2nd rowVALLE
3rd rowBOGOTÁ
4th rowSANTANDER
5th rowQUINDIO

Common Values

ValueCountFrequency (%)
BOGOTÁ 549976
45.2%
ANTIOQUIA 142356
 
11.7%
ATLANTICO 76332
 
6.3%
VALLE 71065
 
5.8%
SANTANDER 52474
 
4.3%
BOLIVAR 35059
 
2.9%
NORTE SANTANDER 32377
 
2.7%
BOYACA 23664
 
1.9%
NARIÑO 21249
 
1.7%
TOLIMA 20079
 
1.6%
Other values (16) 192851
 
15.8%

Length

2025-05-21T17:28:52.408084image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
bogotá 549976
43.7%
antioquia 142356
 
11.3%
santander 84851
 
6.7%
atlantico 76332
 
6.1%
valle 71065
 
5.6%
bolivar 35059
 
2.8%
norte 32377
 
2.6%
boyaca 23664
 
1.9%
nariño 21249
 
1.7%
tolima 20079
 
1.6%
Other values (17) 201782
 
16.0%

Most occurring characters

ValueCountFrequency (%)
O 1522229
17.7%
A 1124245
13.1%
T 992895
11.5%
B 627021
7.3%
G 575916
 
6.7%
Á 549976
 
6.4%
I 527977
 
6.1%
N 513604
 
6.0%
L 349195
 
4.1%
R 285928
 
3.3%
Other values (14) 1530585
17.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 8599571
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
O 1522229
17.7%
A 1124245
13.1%
T 992895
11.5%
B 627021
7.3%
G 575916
 
6.7%
Á 549976
 
6.4%
I 527977
 
6.1%
N 513604
 
6.0%
L 349195
 
4.1%
R 285928
 
3.3%
Other values (14) 1530585
17.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 8599571
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
O 1522229
17.7%
A 1124245
13.1%
T 992895
11.5%
B 627021
7.3%
G 575916
 
6.7%
Á 549976
 
6.4%
I 527977
 
6.1%
N 513604
 
6.0%
L 349195
 
4.1%
R 285928
 
3.3%
Other values (14) 1530585
17.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 8599571
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
O 1522229
17.7%
A 1124245
13.1%
T 992895
11.5%
B 627021
7.3%
G 575916
 
6.7%
Á 549976
 
6.4%
I 527977
 
6.1%
N 513604
 
6.0%
L 349195
 
4.1%
R 285928
 
3.3%
Other values (14) 1530585
17.8%

ESTU_INST_CODMUNICIPIO
Real number (ℝ)

High correlation 

Distinct61
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean23877.768
Minimum5001
Maximum86001
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size9.3 MiB
2025-05-21T17:28:52.607996image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum5001
5-th percentile5001
Q111001
median11001
Q325307
95-th percentile76001
Maximum86001
Range81000
Interquartile range (IQR)14306

Descriptive statistics

Standard deviation23626.376
Coefficient of variation (CV)0.98947175
Kurtosis-0.011761535
Mean23877.768
Median Absolute Deviation (MAD)3000
Skewness1.2934403
Sum2.9070752 × 1010
Variance5.5820566 × 108
MonotonicityNot monotonic
2025-05-21T17:28:52.907681image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11001 549976
45.2%
5001 124659
 
10.2%
8001 75421
 
6.2%
76001 62772
 
5.2%
68001 45172
 
3.7%
13001 35059
 
2.9%
52001 21249
 
1.7%
15001 19675
 
1.6%
66001 18546
 
1.5%
23001 18322
 
1.5%
Other values (51) 246631
20.3%
ValueCountFrequency (%)
5001 124659
10.2%
5034 295
 
< 0.1%
5045 93
 
< 0.1%
5088 1868
 
0.2%
5129 1075
 
0.1%
5148 519
 
< 0.1%
5154 171
 
< 0.1%
5266 2407
 
0.2%
5579 35
 
< 0.1%
5615 3622
 
0.3%
ValueCountFrequency (%)
86001 1038
 
0.1%
85001 1052
 
0.1%
76834 2693
 
0.2%
76622 1353
 
0.1%
76520 2681
 
0.2%
76147 110
 
< 0.1%
76111 103
 
< 0.1%
76109 1353
 
0.1%
76001 62772
5.2%
73268 2625
 
0.2%
Distinct61
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size103.9 MiB
2025-05-21T17:28:53.341372image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length27
Median length11
Mean length9.7144508
Min length4

Characters and Unicode

Total characters11827169
Distinct characters31
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowBOGOTÁ D.C.
2nd rowCALI
3rd rowBOGOTÁ D.C.
4th rowBARRANCABERMEJA
5th rowARMENIA
ValueCountFrequency (%)
bogotá 549976
29.4%
d.c 549976
29.4%
medellín 124659
 
6.7%
barranquilla 75421
 
4.0%
cali 62772
 
3.4%
bucaramanga 45172
 
2.4%
de 39552
 
2.1%
cartagena 35059
 
1.9%
indias 35059
 
1.9%
pasto 21249
 
1.1%
Other values (65) 331486
17.7%
2025-05-21T17:28:53.807653image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
O 1251335
 
10.6%
. 1099952
 
9.3%
A 1071269
 
9.1%
D 779556
 
6.6%
C 778082
 
6.6%
B 714216
 
6.0%
T 707004
 
6.0%
G 678091
 
5.7%
652899
 
5.5%
Á 577912
 
4.9%
Other values (21) 3516853
29.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 11827169
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
O 1251335
 
10.6%
. 1099952
 
9.3%
A 1071269
 
9.1%
D 779556
 
6.6%
C 778082
 
6.6%
B 714216
 
6.0%
T 707004
 
6.0%
G 678091
 
5.7%
652899
 
5.5%
Á 577912
 
4.9%
Other values (21) 3516853
29.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 11827169
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
O 1251335
 
10.6%
. 1099952
 
9.3%
A 1071269
 
9.1%
D 779556
 
6.6%
C 778082
 
6.6%
B 714216
 
6.0%
T 707004
 
6.0%
G 678091
 
5.7%
652899
 
5.5%
Á 577912
 
4.9%
Other values (21) 3516853
29.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 11827169
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
O 1251335
 
10.6%
. 1099952
 
9.3%
A 1071269
 
9.1%
D 779556
 
6.6%
C 778082
 
6.6%
B 714216
 
6.0%
T 707004
 
6.0%
G 678091
 
5.7%
652899
 
5.5%
Á 577912
 
4.9%
Other values (21) 3516853
29.7%
Distinct1023
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size103.0 MiB
2025-05-21T17:28:54.090953image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length109
Median length87
Mean length21.830439
Min length4

Characters and Unicode

Total characters26578166
Distinct characters62
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique35 ?
Unique (%)< 0.1%

Sample

1st rowHOTELERIA Y TURISMO
2nd rowCONTADURIA PUBLICA
3rd rowCONTADURÍA PÚBLICA
4th rowINGENIERIA AMBIENTAL Y DE SANEAMIENTO
5th rowCIENCIAS DE LA INFORMACION Y LA DOCUMENTACION
ValueCountFrequency (%)
de 236636
 
7.7%
ingenieria 226863
 
7.3%
en 194387
 
6.3%
administracion 143489
 
4.6%
empresas 142305
 
4.6%
y 115075
 
3.7%
licenciatura 112974
 
3.7%
derecho 93800
 
3.0%
publica 81365
 
2.6%
administración 79359
 
2.6%
Other values (588) 1663703
53.8%
2025-05-21T17:28:54.910687image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
I 3645371
13.7%
A 3051354
11.5%
E 2598430
9.8%
N 2346149
8.8%
1881320
 
7.1%
C 1840251
 
6.9%
R 1611732
 
6.1%
O 1473811
 
5.5%
S 1383635
 
5.2%
D 1077333
 
4.1%
Other values (52) 5668780
21.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 26578166
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
I 3645371
13.7%
A 3051354
11.5%
E 2598430
9.8%
N 2346149
8.8%
1881320
 
7.1%
C 1840251
 
6.9%
R 1611732
 
6.1%
O 1473811
 
5.5%
S 1383635
 
5.2%
D 1077333
 
4.1%
Other values (52) 5668780
21.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 26578166
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
I 3645371
13.7%
A 3051354
11.5%
E 2598430
9.8%
N 2346149
8.8%
1881320
 
7.1%
C 1840251
 
6.9%
R 1611732
 
6.1%
O 1473811
 
5.5%
S 1383635
 
5.2%
D 1077333
 
4.1%
Other values (52) 5668780
21.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 26578166
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
I 3645371
13.7%
A 3051354
11.5%
E 2598430
9.8%
N 2346149
8.8%
1881320
 
7.1%
C 1840251
 
6.9%
R 1611732
 
6.1%
O 1473811
 
5.5%
S 1383635
 
5.2%
D 1077333
 
4.1%
Other values (52) 5668780
21.3%

ESTU_PRGM_DEPARTAMENTO
Categorical

High correlation 

Distinct31
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size90.2 MiB
BOGOTÁ
492741 
ANTIOQUIA
145737 
VALLE
78407 
ATLANTICO
72823 
SANTANDER
49677 
Other values (26)
378097 

Length

Max length15
Median length12
Mean length7.1213217
Min length4

Characters and Unicode

Total characters8670081
Distinct characters25
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowBOGOTÁ
2nd rowVALLE
3rd rowBOGOTÁ
4th rowSANTANDER
5th rowQUINDIO

Common Values

ValueCountFrequency (%)
BOGOTÁ 492741
40.5%
ANTIOQUIA 145737
 
12.0%
VALLE 78407
 
6.4%
ATLANTICO 72823
 
6.0%
SANTANDER 49677
 
4.1%
NORTE SANTANDER 39602
 
3.3%
BOLIVAR 37019
 
3.0%
BOYACA 25370
 
2.1%
CUNDINAMARCA 24593
 
2.0%
NARIÑO 23641
 
1.9%
Other values (21) 227872
18.7%

Length

2025-05-21T17:28:55.110303image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
bogotá 492741
38.9%
antioquia 145737
 
11.5%
santander 89279
 
7.1%
valle 78407
 
6.2%
atlantico 72823
 
5.8%
norte 39602
 
3.1%
bolivar 37019
 
2.9%
boyaca 25370
 
2.0%
cundinamarca 24593
 
1.9%
nariño 23641
 
1.9%
Other values (23) 236825
18.7%

Most occurring characters

ValueCountFrequency (%)
O 1428089
16.5%
A 1218018
14.0%
T 954554
11.0%
B 577314
 
6.7%
I 548828
 
6.3%
N 543960
 
6.3%
G 518375
 
6.0%
Á 492741
 
5.7%
L 374000
 
4.3%
R 324568
 
3.7%
Other values (15) 1689634
19.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 8670081
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
O 1428089
16.5%
A 1218018
14.0%
T 954554
11.0%
B 577314
 
6.7%
I 548828
 
6.3%
N 543960
 
6.3%
G 518375
 
6.0%
Á 492741
 
5.7%
L 374000
 
4.3%
R 324568
 
3.7%
Other values (15) 1689634
19.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 8670081
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
O 1428089
16.5%
A 1218018
14.0%
T 954554
11.0%
B 577314
 
6.7%
I 548828
 
6.3%
N 543960
 
6.3%
G 518375
 
6.0%
Á 492741
 
5.7%
L 374000
 
4.3%
R 324568
 
3.7%
Other values (15) 1689634
19.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 8670081
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
O 1428089
16.5%
A 1218018
14.0%
T 954554
11.0%
B 577314
 
6.7%
I 548828
 
6.3%
N 543960
 
6.3%
G 518375
 
6.0%
Á 492741
 
5.7%
L 374000
 
4.3%
R 324568
 
3.7%
Other values (15) 1689634
19.5%

ESTU_PRGM_CODMUNICIPIO
Real number (ℝ)

High correlation 

Distinct163
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean25422.939
Minimum5001
Maximum97001
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size9.3 MiB
2025-05-21T17:28:55.373274image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum5001
5-th percentile5001
Q111001
median11001
Q344001
95-th percentile76001
Maximum97001
Range92000
Interquartile range (IQR)33000

Descriptive statistics

Standard deviation24294.768
Coefficient of variation (CV)0.95562389
Kurtosis-0.36156598
Mean25422.939
Median Absolute Deviation (MAD)4000
Skewness1.13993
Sum3.0951971 × 1010
Variance5.9023575 × 108
MonotonicityNot monotonic
2025-05-21T17:28:55.600522image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11001 492741
40.5%
5001 122299
 
10.0%
8001 71745
 
5.9%
76001 62747
 
5.2%
68001 41766
 
3.4%
13001 36967
 
3.0%
52001 22491
 
1.8%
23001 22096
 
1.8%
66001 21566
 
1.8%
17001 20253
 
1.7%
Other values (153) 302811
24.9%
ValueCountFrequency (%)
5001 122299
10.0%
5031 138
 
< 0.1%
5034 443
 
< 0.1%
5042 86
 
< 0.1%
5045 1165
 
0.1%
5059 5
 
< 0.1%
5088 4126
 
0.3%
5129 1075
 
0.1%
5147 181
 
< 0.1%
5148 1094
 
0.1%
ValueCountFrequency (%)
97001 21
 
< 0.1%
95001 44
 
< 0.1%
91263 1
 
< 0.1%
91001 65
 
< 0.1%
88001 14
 
< 0.1%
86749 286
 
< 0.1%
86568 19
 
< 0.1%
86001 1127
 
0.1%
85010 86
 
< 0.1%
85001 3140
0.3%
Distinct160
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size102.3 MiB
2025-05-21T17:28:55.977601image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length27
Median length22
Mean length9.6180724
Min length4

Characters and Unicode

Total characters11709830
Distinct characters32
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique11 ?
Unique (%)< 0.1%

Sample

1st rowBOGOTÁ D.C.
2nd rowTULUÁ
3rd rowBOGOTÁ D.C.
4th rowBARRANCABERMEJA
5th rowARMENIA
ValueCountFrequency (%)
bogotá 492741
26.9%
d.c 492741
26.9%
medellín 122299
 
6.7%
barranquilla 71745
 
3.9%
cali 62747
 
3.4%
de 46182
 
2.5%
bucaramanga 41766
 
2.3%
cartagena 36967
 
2.0%
indias 36967
 
2.0%
pasto 22491
 
1.2%
Other values (170) 403210
22.0%
2025-05-21T17:28:57.044094image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
O 1167621
 
10.0%
A 1150251
 
9.8%
. 985482
 
8.4%
C 770786
 
6.6%
D 740453
 
6.3%
T 676821
 
5.8%
B 658004
 
5.6%
G 616957
 
5.3%
612374
 
5.2%
L 609616
 
5.2%
Other values (22) 3721465
31.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 11709830
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
O 1167621
 
10.0%
A 1150251
 
9.8%
. 985482
 
8.4%
C 770786
 
6.6%
D 740453
 
6.3%
T 676821
 
5.8%
B 658004
 
5.6%
G 616957
 
5.3%
612374
 
5.2%
L 609616
 
5.2%
Other values (22) 3721465
31.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 11709830
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
O 1167621
 
10.0%
A 1150251
 
9.8%
. 985482
 
8.4%
C 770786
 
6.6%
D 740453
 
6.3%
T 676821
 
5.8%
B 658004
 
5.6%
G 616957
 
5.3%
612374
 
5.2%
L 609616
 
5.2%
Other values (22) 3721465
31.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 11709830
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
O 1167621
 
10.0%
A 1150251
 
9.8%
. 985482
 
8.4%
C 770786
 
6.6%
D 740453
 
6.3%
T 676821
 
5.8%
B 658004
 
5.6%
G 616957
 
5.3%
612374
 
5.2%
L 609616
 
5.2%
Other values (22) 3721465
31.8%

ESTU_NIVEL_PRGM_ACADEMICO
Categorical

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size81.3 MiB
UNIVERSITARIO
1217482 

Length

Max length13
Median length13
Mean length13
Min length13

Characters and Unicode

Total characters15827266
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowUNIVERSITARIO
2nd rowUNIVERSITARIO
3rd rowUNIVERSITARIO
4th rowUNIVERSITARIO
5th rowUNIVERSITARIO

Common Values

ValueCountFrequency (%)
UNIVERSITARIO 1217482
100.0%

Length

2025-05-21T17:28:57.190758image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-21T17:28:57.273830image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
universitario 1217482
100.0%

Most occurring characters

ValueCountFrequency (%)
I 3652446
23.1%
R 2434964
15.4%
N 1217482
 
7.7%
U 1217482
 
7.7%
V 1217482
 
7.7%
E 1217482
 
7.7%
S 1217482
 
7.7%
T 1217482
 
7.7%
A 1217482
 
7.7%
O 1217482
 
7.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 15827266
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
I 3652446
23.1%
R 2434964
15.4%
N 1217482
 
7.7%
U 1217482
 
7.7%
V 1217482
 
7.7%
E 1217482
 
7.7%
S 1217482
 
7.7%
T 1217482
 
7.7%
A 1217482
 
7.7%
O 1217482
 
7.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 15827266
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
I 3652446
23.1%
R 2434964
15.4%
N 1217482
 
7.7%
U 1217482
 
7.7%
V 1217482
 
7.7%
E 1217482
 
7.7%
S 1217482
 
7.7%
T 1217482
 
7.7%
A 1217482
 
7.7%
O 1217482
 
7.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 15827266
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
I 3652446
23.1%
R 2434964
15.4%
N 1217482
 
7.7%
U 1217482
 
7.7%
V 1217482
 
7.7%
E 1217482
 
7.7%
S 1217482
 
7.7%
T 1217482
 
7.7%
A 1217482
 
7.7%
O 1217482
 
7.7%

ESTU_METODO_PRGM
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size78.1 MiB
PRESENCIAL
950741 
DISTANCIA
189581 
DISTANCIA VITUAL
 
77160

Length

Max length16
Median length10
Mean length10.224545
Min length9

Characters and Unicode

Total characters12448199
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPRESENCIAL
2nd rowPRESENCIAL
3rd rowDISTANCIA
4th rowPRESENCIAL
5th rowDISTANCIA VITUAL

Common Values

ValueCountFrequency (%)
PRESENCIAL 950741
78.1%
DISTANCIA 189581
 
15.6%
DISTANCIA VITUAL 77160
 
6.3%

Length

2025-05-21T17:28:57.384143image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-21T17:28:57.493635image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
presencial 950741
73.4%
distancia 266741
 
20.6%
vitual 77160
 
6.0%

Most occurring characters

ValueCountFrequency (%)
E 1901482
15.3%
I 1561383
12.5%
A 1561383
12.5%
C 1217482
9.8%
N 1217482
9.8%
S 1217482
9.8%
L 1027901
8.3%
R 950741
7.6%
P 950741
7.6%
T 343901
 
2.8%
Other values (4) 498221
 
4.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 12448199
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
E 1901482
15.3%
I 1561383
12.5%
A 1561383
12.5%
C 1217482
9.8%
N 1217482
9.8%
S 1217482
9.8%
L 1027901
8.3%
R 950741
7.6%
P 950741
7.6%
T 343901
 
2.8%
Other values (4) 498221
 
4.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 12448199
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
E 1901482
15.3%
I 1561383
12.5%
A 1561383
12.5%
C 1217482
9.8%
N 1217482
9.8%
S 1217482
9.8%
L 1027901
8.3%
R 950741
7.6%
P 950741
7.6%
T 343901
 
2.8%
Other values (4) 498221
 
4.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 12448199
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
E 1901482
15.3%
I 1561383
12.5%
A 1561383
12.5%
C 1217482
9.8%
N 1217482
9.8%
S 1217482
9.8%
L 1027901
8.3%
R 950741
7.6%
P 950741
7.6%
T 343901
 
2.8%
Other values (4) 498221
 
4.0%
Distinct8
Distinct (%)< 0.1%
Missing12065
Missing (%)1.0%
Memory size139.6 MiB
Entre 1 millón y menos de 2.5 millones
350406 
Entre 2.5 millones y menos de 4 millones
220554 
Menos de 500 mil
138782 
Entre 500 mil y menos de 1 millón
129353 
Entre 4 millones y menos de 5.5 millones
124018 
Other values (3)
242304 

Length

Max length40
Median length38
Mean length32.582378
Min length16

Characters and Unicode

Total characters39275352
Distinct characters30
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowEntre 2.5 millones y menos de 4 millones
2nd rowMenos de 500 mil
3rd rowEntre 1 millón y menos de 2.5 millones
4th rowEntre 500 mil y menos de 1 millón
5th rowEntre 500 mil y menos de 1 millón

Common Values

ValueCountFrequency (%)
Entre 1 millón y menos de 2.5 millones 350406
28.8%
Entre 2.5 millones y menos de 4 millones 220554
18.1%
Menos de 500 mil 138782
 
11.4%
Entre 500 mil y menos de 1 millón 129353
 
10.6%
Entre 4 millones y menos de 5.5 millones 124018
 
10.2%
Más de 7 millones 123036
 
10.1%
Entre 5.5 millones y menos de 7 millones 68205
 
5.6%
No pagó matrícula 51063
 
4.2%
(Missing) 12065
 
1.0%

Length

2025-05-21T17:28:57.628245image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-21T17:28:57.760386image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
millones 1298996
15.6%
de 1154354
13.8%
menos 1031318
12.4%
entre 892536
10.7%
y 892536
10.7%
2.5 570960
6.8%
millón 479759
 
5.8%
1 479759
 
5.8%
4 344572
 
4.1%
500 268135
 
3.2%
Other values (7) 927824
11.1%

Most occurring characters

ValueCountFrequency (%)
7135332
18.2%
e 4377204
11.1%
l 3876708
9.9%
n 3702609
9.4%
m 2990489
 
7.6%
s 2453350
 
6.2%
o 2381377
 
6.1%
i 2046890
 
5.2%
5 1223541
 
3.1%
d 1154354
 
2.9%
Other values (20) 7933498
20.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 39275352
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
7135332
18.2%
e 4377204
11.1%
l 3876708
9.9%
n 3702609
9.4%
m 2990489
 
7.6%
s 2453350
 
6.2%
o 2381377
 
6.1%
i 2046890
 
5.2%
5 1223541
 
3.1%
d 1154354
 
2.9%
Other values (20) 7933498
20.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 39275352
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
7135332
18.2%
e 4377204
11.1%
l 3876708
9.9%
n 3702609
9.4%
m 2990489
 
7.6%
s 2453350
 
6.2%
o 2381377
 
6.1%
i 2046890
 
5.2%
5 1223541
 
3.1%
d 1154354
 
2.9%
Other values (20) 7933498
20.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 39275352
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
7135332
18.2%
e 4377204
11.1%
l 3876708
9.9%
n 3702609
9.4%
m 2990489
 
7.6%
s 2453350
 
6.2%
o 2381377
 
6.1%
i 2046890
 
5.2%
5 1223541
 
3.1%
d 1154354
 
2.9%
Other values (20) 7933498
20.2%
Distinct74
Distinct (%)< 0.1%
Missing104
Missing (%)< 0.1%
Memory size97.0 MiB
2025-05-21T17:28:58.024291image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length23
Median length6
Mean length6.7202225
Min length4

Characters and Unicode

Total characters8181051
Distinct characters29
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowBOGOTÁ
2nd rowVALLE
3rd rowTOLIMA
4th rowSANTANDER
5th rowBOGOTÁ
ValueCountFrequency (%)
bogotá 737228
59.1%
antioquia 73041
 
5.9%
santander 54240
 
4.3%
valle 49682
 
4.0%
atlantico 42522
 
3.4%
cundinamarca 33113
 
2.7%
norte 22671
 
1.8%
bolivar 21998
 
1.8%
cordoba 17748
 
1.4%
boyaca 16956
 
1.4%
Other values (80) 178438
 
14.3%
2025-05-21T17:28:58.541189image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
O 1744881
21.3%
T 1009068
12.3%
A 811457
9.9%
B 794230
9.7%
G 754496
9.2%
Á 737238
9.0%
N 353263
 
4.3%
I 329461
 
4.0%
L 239122
 
2.9%
C 229970
 
2.8%
Other values (19) 1177865
14.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 8181051
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
O 1744881
21.3%
T 1009068
12.3%
A 811457
9.9%
B 794230
9.7%
G 754496
9.2%
Á 737238
9.0%
N 353263
 
4.3%
I 329461
 
4.0%
L 239122
 
2.9%
C 229970
 
2.8%
Other values (19) 1177865
14.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 8181051
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
O 1744881
21.3%
T 1009068
12.3%
A 811457
9.9%
B 794230
9.7%
G 754496
9.2%
Á 737238
9.0%
N 353263
 
4.3%
I 329461
 
4.0%
L 239122
 
2.9%
C 229970
 
2.8%
Other values (19) 1177865
14.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 8181051
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
O 1744881
21.3%
T 1009068
12.3%
A 811457
9.9%
B 794230
9.7%
G 754496
9.2%
Á 737238
9.0%
N 353263
 
4.3%
I 329461
 
4.0%
L 239122
 
2.9%
C 229970
 
2.8%
Other values (19) 1177865
14.4%

ESTU_COD_MCPIO_PRESENTACION
Real number (ℝ)

High correlation 

Distinct165
Distinct (%)< 0.1%
Missing104
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean21160.515
Minimum1
Maximum99001
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size9.3 MiB
2025-05-21T17:28:58.744158image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile5360
Q111001
median11001
Q319001
95-th percentile73268
Maximum99001
Range99000
Interquartile range (IQR)8000

Descriptive statistics

Standard deviation21098.584
Coefficient of variation (CV)0.99707324
Kurtosis1.5758812
Mean21160.515
Median Absolute Deviation (MAD)0
Skewness1.7599896
Sum2.5760346 × 1010
Variance4.4515023 × 108
MonotonicityNot monotonic
2025-05-21T17:28:58.989599image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11001 737228
60.6%
5001 43693
 
3.6%
8001 38913
 
3.2%
76001 33457
 
2.7%
68001 22264
 
1.8%
13001 20343
 
1.7%
23001 16204
 
1.3%
54001 14847
 
1.2%
73001 14331
 
1.2%
52001 13699
 
1.1%
Other values (155) 262399
 
21.6%
ValueCountFrequency (%)
1 75
< 0.1%
2 43
 
< 0.1%
3 75
< 0.1%
4 53
< 0.1%
5 9
 
< 0.1%
6 123
< 0.1%
7 41
 
< 0.1%
8 10
 
< 0.1%
9 81
< 0.1%
10 6
 
< 0.1%
ValueCountFrequency (%)
99001 158
 
< 0.1%
97001 177
 
< 0.1%
95001 490
 
< 0.1%
94001 119
 
< 0.1%
91001 262
 
< 0.1%
88001 447
 
< 0.1%
86749 310
 
< 0.1%
86568 447
 
< 0.1%
86320 252
 
< 0.1%
86001 1445
0.1%
Distinct164
Distinct (%)< 0.1%
Missing104
Missing (%)< 0.1%
Memory size108.7 MiB
2025-05-21T17:28:59.600474image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length23
Median length11
Mean length10.034583
Min length4

Characters and Unicode

Total characters12215881
Distinct characters35
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4 ?
Unique (%)< 0.1%

Sample

1st rowBOGOTÁ D.C.
2nd rowTULUÁ
3rd rowIBAGUÉ
4th rowBARRANCABERMEJA
5th rowBOGOTÁ D.C.
ValueCountFrequency (%)
bogotá 737228
36.3%
d.c 737228
36.3%
medellín 43693
 
2.2%
barranquilla 38913
 
1.9%
cali 33457
 
1.6%
de 27229
 
1.3%
bucaramanga 22264
 
1.1%
cartagena 20343
 
1.0%
indias 20343
 
1.0%
montería 16204
 
0.8%
Other values (184) 334335
16.5%
2025-05-21T17:29:00.339366image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
O 1630242
13.3%
. 1474456
12.1%
C 934520
 
7.7%
D 878719
 
7.2%
T 876574
 
7.2%
B 846420
 
6.9%
G 830180
 
6.8%
813859
 
6.7%
A 770825
 
6.3%
Á 765926
 
6.3%
Other values (25) 2394160
19.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 12215881
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
O 1630242
13.3%
. 1474456
12.1%
C 934520
 
7.7%
D 878719
 
7.2%
T 876574
 
7.2%
B 846420
 
6.9%
G 830180
 
6.8%
813859
 
6.7%
A 770825
 
6.3%
Á 765926
 
6.3%
Other values (25) 2394160
19.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 12215881
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
O 1630242
13.3%
. 1474456
12.1%
C 934520
 
7.7%
D 878719
 
7.2%
T 876574
 
7.2%
B 846420
 
6.9%
G 830180
 
6.8%
813859
 
6.7%
A 770825
 
6.3%
Á 765926
 
6.3%
Other values (25) 2394160
19.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 12215881
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
O 1630242
13.3%
. 1474456
12.1%
C 934520
 
7.7%
D 878719
 
7.2%
T 876574
 
7.2%
B 846420
 
6.9%
G 830180
 
6.8%
813859
 
6.7%
A 770825
 
6.3%
Á 765926
 
6.3%
Other values (25) 2394160
19.6%

ESTU_PAGOMATRICULABECA
Categorical

Missing 

Distinct2
Distinct (%)< 0.1%
Missing12550
Missing (%)1.0%
Memory size68.6 MiB
No
949842 
Si
255090 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters2409864
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNo
2nd rowNo
3rd rowNo
4th rowNo
5th rowNo

Common Values

ValueCountFrequency (%)
No 949842
78.0%
Si 255090
 
21.0%
(Missing) 12550
 
1.0%

Length

2025-05-21T17:29:00.489355image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-21T17:29:00.623738image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
no 949842
78.8%
si 255090
 
21.2%

Most occurring characters

ValueCountFrequency (%)
N 949842
39.4%
o 949842
39.4%
S 255090
 
10.6%
i 255090
 
10.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2409864
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
N 949842
39.4%
o 949842
39.4%
S 255090
 
10.6%
i 255090
 
10.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2409864
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
N 949842
39.4%
o 949842
39.4%
S 255090
 
10.6%
i 255090
 
10.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2409864
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
N 949842
39.4%
o 949842
39.4%
S 255090
 
10.6%
i 255090
 
10.6%

ESTU_PAGOMATRICULACREDITO
Categorical

Missing 

Distinct2
Distinct (%)< 0.1%
Missing12470
Missing (%)1.0%
Memory size68.6 MiB
No
822939 
Si
382073 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters2410024
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNo
2nd rowNo
3rd rowNo
4th rowNo
5th rowNo

Common Values

ValueCountFrequency (%)
No 822939
67.6%
Si 382073
31.4%
(Missing) 12470
 
1.0%

Length

2025-05-21T17:29:00.784322image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-21T17:29:00.907057image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
no 822939
68.3%
si 382073
31.7%

Most occurring characters

ValueCountFrequency (%)
N 822939
34.1%
o 822939
34.1%
S 382073
15.9%
i 382073
15.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2410024
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
N 822939
34.1%
o 822939
34.1%
S 382073
15.9%
i 382073
15.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2410024
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
N 822939
34.1%
o 822939
34.1%
S 382073
15.9%
i 382073
15.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2410024
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
N 822939
34.1%
o 822939
34.1%
S 382073
15.9%
i 382073
15.9%

ESTU_HORASSEMANATRABAJA
Categorical

Missing 

Distinct5
Distinct (%)< 0.1%
Missing55467
Missing (%)4.6%
Memory size99.4 MiB
Más de 30 horas
440286 
Entre 11 y 20 horas
203402 
0
200324 
Entre 21 y 30 horas
160740 
Menos de 10 horas
157263 

Length

Max length19
Median length17
Mean length14.110647
Min length1

Characters and Unicode

Total characters16396783
Distinct characters18
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowEntre 11 y 20 horas
2nd rowEntre 21 y 30 horas
3rd rowMás de 30 horas
4th row0
5th rowEntre 21 y 30 horas

Common Values

ValueCountFrequency (%)
Más de 30 horas 440286
36.2%
Entre 11 y 20 horas 203402
16.7%
0 200324
16.5%
Entre 21 y 30 horas 160740
 
13.2%
Menos de 10 horas 157263
 
12.9%
(Missing) 55467
 
4.6%

Length

2025-05-21T17:29:01.078343image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-21T17:29:01.226500image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
horas 961691
21.8%
30 601026
13.6%
de 597549
13.5%
más 440286
10.0%
entre 364142
 
8.3%
y 364142
 
8.3%
11 203402
 
4.6%
20 203402
 
4.6%
0 200324
 
4.5%
21 160740
 
3.6%
Other values (2) 314526
 
7.1%

Most occurring characters

ValueCountFrequency (%)
3249215
19.8%
s 1559240
9.5%
r 1325833
 
8.1%
0 1162015
 
7.1%
o 1118954
 
6.8%
e 1118954
 
6.8%
a 961691
 
5.9%
h 961691
 
5.9%
1 724807
 
4.4%
3 601026
 
3.7%
Other values (8) 3613357
22.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 16396783
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
3249215
19.8%
s 1559240
9.5%
r 1325833
 
8.1%
0 1162015
 
7.1%
o 1118954
 
6.8%
e 1118954
 
6.8%
a 961691
 
5.9%
h 961691
 
5.9%
1 724807
 
4.4%
3 601026
 
3.7%
Other values (8) 3613357
22.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 16396783
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
3249215
19.8%
s 1559240
9.5%
r 1325833
 
8.1%
0 1162015
 
7.1%
o 1118954
 
6.8%
e 1118954
 
6.8%
a 961691
 
5.9%
h 961691
 
5.9%
1 724807
 
4.4%
3 601026
 
3.7%
Other values (8) 3613357
22.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 16396783
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
3249215
19.8%
s 1559240
9.5%
r 1325833
 
8.1%
0 1162015
 
7.1%
o 1118954
 
6.8%
e 1118954
 
6.8%
a 961691
 
5.9%
h 961691
 
5.9%
1 724807
 
4.4%
3 601026
 
3.7%
Other values (8) 3613357
22.0%

ESTU_SNIES_PRGMACADEMICO
Real number (ℝ)

Distinct4583
Distinct (%)0.4%
Missing2
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean39264.937
Minimum1
Maximum110832
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size9.3 MiB
2025-05-21T17:29:01.573501image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile584
Q12995
median15770
Q390834
95-th percentile104545
Maximum110832
Range110831
Interquartile range (IQR)87839

Descriptive statistics

Standard deviation40653.837
Coefficient of variation (CV)1.0353725
Kurtosis-1.3674693
Mean39264.937
Median Absolute Deviation (MAD)14900
Skewness0.57077273
Sum4.7804276 × 1010
Variance1.6527345 × 109
MonotonicityNot monotonic
2025-05-21T17:29:01.856943image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
91236 17375
 
1.4%
90399 14442
 
1.2%
91141 14083
 
1.2%
91237 12120
 
1.0%
91334 9446
 
0.8%
1697 9234
 
0.8%
103186 8792
 
0.7%
3274 8231
 
0.7%
90962 6930
 
0.6%
101389 6409
 
0.5%
Other values (4573) 1110418
91.2%
ValueCountFrequency (%)
1 319
 
< 0.1%
2 315
 
< 0.1%
3 183
 
< 0.1%
4 194
 
< 0.1%
5 288
 
< 0.1%
6 147
 
< 0.1%
7 390
 
< 0.1%
8 147
 
< 0.1%
9 984
0.1%
10 305
 
< 0.1%
ValueCountFrequency (%)
110832 1
 
< 0.1%
110771 4
 
< 0.1%
110700 7
 
< 0.1%
110698 3
 
< 0.1%
110359 1
 
< 0.1%
110357 8
 
< 0.1%
110354 1
 
< 0.1%
110286 3
 
< 0.1%
110279 1
 
< 0.1%
110234 1216
0.1%

ESTU_PRIVADO_LIBERTAD
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size67.3 MiB
N
1217416 
S
 
66

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1217482
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowN
2nd rowN
3rd rowN
4th rowN
5th rowN

Common Values

ValueCountFrequency (%)
N 1217416
> 99.9%
S 66
 
< 0.1%

Length

2025-05-21T17:29:02.105824image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-21T17:29:02.336388image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
n 1217416
> 99.9%
s 66
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
N 1217416
> 99.9%
S 66
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1217482
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
N 1217416
> 99.9%
S 66
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1217482
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
N 1217416
> 99.9%
S 66
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1217482
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
N 1217416
> 99.9%
S 66
 
< 0.1%
Distinct86
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size75.5 MiB
2025-05-21T17:29:02.694632image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length40
Median length8
Mean length8.0003926
Min length4

Characters and Unicode

Total characters9740334
Distinct characters33
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique22 ?
Unique (%)< 0.1%

Sample

1st rowCOLOMBIA
2nd rowCOLOMBIA
3rd rowCOLOMBIA
4th rowCOLOMBIA
5th rowCOLOMBIA
ValueCountFrequency (%)
colombia 1215091
99.8%
venezuela 885
 
0.1%
perú 182
 
< 0.1%
ecuador 170
 
< 0.1%
francia 138
 
< 0.1%
estados 113
 
< 0.1%
unidos 111
 
< 0.1%
panamá 86
 
< 0.1%
méxico 71
 
< 0.1%
alemania 67
 
< 0.1%
Other values (99) 903
 
0.1%
2025-05-21T17:29:03.226617image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
O 2430962
25.0%
A 1217971
12.5%
L 1216383
12.5%
I 1216017
12.5%
C 1215777
12.5%
M 1215406
12.5%
B 1215266
12.5%
E 3574
 
< 0.1%
N 1585
 
< 0.1%
U 1355
 
< 0.1%
Other values (23) 6038
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 9740334
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
O 2430962
25.0%
A 1217971
12.5%
L 1216383
12.5%
I 1216017
12.5%
C 1215777
12.5%
M 1215406
12.5%
B 1215266
12.5%
E 3574
 
< 0.1%
N 1585
 
< 0.1%
U 1355
 
< 0.1%
Other values (23) 6038
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 9740334
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
O 2430962
25.0%
A 1217971
12.5%
L 1216383
12.5%
I 1216017
12.5%
C 1215777
12.5%
M 1215406
12.5%
B 1215266
12.5%
E 3574
 
< 0.1%
N 1585
 
< 0.1%
U 1355
 
< 0.1%
Other values (23) 6038
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 9740334
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
O 2430962
25.0%
A 1217971
12.5%
L 1216383
12.5%
I 1216017
12.5%
C 1215777
12.5%
M 1215406
12.5%
B 1215266
12.5%
E 3574
 
< 0.1%
N 1585
 
< 0.1%
U 1355
 
< 0.1%
Other values (23) 6038
 
0.1%

ESTU_ESTUDIANTE
Categorical

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size77.8 MiB
ESTUDIANTE
1217482 

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters12174820
Distinct characters8
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowESTUDIANTE
2nd rowESTUDIANTE
3rd rowESTUDIANTE
4th rowESTUDIANTE
5th rowESTUDIANTE

Common Values

ValueCountFrequency (%)
ESTUDIANTE 1217482
100.0%

Length

2025-05-21T17:29:03.419908image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-21T17:29:03.588949image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
estudiante 1217482
100.0%

Most occurring characters

ValueCountFrequency (%)
E 2434964
20.0%
T 2434964
20.0%
S 1217482
10.0%
U 1217482
10.0%
D 1217482
10.0%
I 1217482
10.0%
A 1217482
10.0%
N 1217482
10.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 12174820
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
E 2434964
20.0%
T 2434964
20.0%
S 1217482
10.0%
U 1217482
10.0%
D 1217482
10.0%
I 1217482
10.0%
A 1217482
10.0%
N 1217482
10.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 12174820
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
E 2434964
20.0%
T 2434964
20.0%
S 1217482
10.0%
U 1217482
10.0%
D 1217482
10.0%
I 1217482
10.0%
A 1217482
10.0%
N 1217482
10.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 12174820
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
E 2434964
20.0%
T 2434964
20.0%
S 1217482
10.0%
U 1217482
10.0%
D 1217482
10.0%
I 1217482
10.0%
A 1217482
10.0%
N 1217482
10.0%

ESTU_GENERO
Categorical

Distinct2
Distinct (%)< 0.1%
Missing119
Missing (%)< 0.1%
Memory size67.3 MiB
F
718715 
M
498648 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1217363
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowF
2nd rowM
3rd rowM
4th rowM
5th rowF

Common Values

ValueCountFrequency (%)
F 718715
59.0%
M 498648
41.0%
(Missing) 119
 
< 0.1%

Length

2025-05-21T17:29:03.763867image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-21T17:29:03.909580image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
f 718715
59.0%
m 498648
41.0%

Most occurring characters

ValueCountFrequency (%)
F 718715
59.0%
M 498648
41.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1217363
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
F 718715
59.0%
M 498648
41.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1217363
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
F 718715
59.0%
M 498648
41.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1217363
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
F 718715
59.0%
M 498648
41.0%

ESTU_COLE_TERMINO
Text

Missing 

Distinct26388
Distinct (%)3.1%
Missing375648
Missing (%)30.9%
Memory size84.2 MiB
2025-05-21T17:29:04.284565image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length100
Median length82
Mean length27.030837
Min length1

Characters and Unicode

Total characters22755478
Distinct characters80
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4328 ?
Unique (%)0.5%

Sample

1st rowSEDE 1 GERMAN PARDO
2nd rowINSTITUTO TECNICO INDUSTRIAL
3rd rowCENT EDUC DIST NUEVA CONSTITUCION
4th rowINSTITUCION EDUCATIVA NORMAL SUPERIOR DE SINCELEJO
5th rowCOLEGIO AMERICANO DE BOGOTA
ValueCountFrequency (%)
de 160191
 
4.6%
sede 148430
 
4.3%
educativa 129266
 
3.7%
117957
 
3.4%
principal 113449
 
3.3%
colegio 108208
 
3.1%
col 105744
 
3.0%
institucion 99946
 
2.9%
san 52643
 
1.5%
educ 52436
 
1.5%
Other values (20999) 2385428
68.7%
2025-05-21T17:29:05.077011image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2658869
11.7%
A 2174974
 
9.6%
I 2094512
 
9.2%
E 1984351
 
8.7%
O 1575060
 
6.9%
N 1392166
 
6.1%
C 1309834
 
5.8%
S 1162029
 
5.1%
T 1146835
 
5.0%
L 1118386
 
4.9%
Other values (70) 6138462
27.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 22755478
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2658869
11.7%
A 2174974
 
9.6%
I 2094512
 
9.2%
E 1984351
 
8.7%
O 1575060
 
6.9%
N 1392166
 
6.1%
C 1309834
 
5.8%
S 1162029
 
5.1%
T 1146835
 
5.0%
L 1118386
 
4.9%
Other values (70) 6138462
27.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 22755478
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2658869
11.7%
A 2174974
 
9.6%
I 2094512
 
9.2%
E 1984351
 
8.7%
O 1575060
 
6.9%
N 1392166
 
6.1%
C 1309834
 
5.8%
S 1162029
 
5.1%
T 1146835
 
5.0%
L 1118386
 
4.9%
Other values (70) 6138462
27.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 22755478
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2658869
11.7%
A 2174974
 
9.6%
I 2094512
 
9.2%
E 1984351
 
8.7%
O 1575060
 
6.9%
N 1392166
 
6.1%
C 1309834
 
5.8%
S 1162029
 
5.1%
T 1146835
 
5.0%
L 1118386
 
4.9%
Other values (70) 6138462
27.0%

ESTU_PAGOMATRICULAPADRES
Categorical

Missing 

Distinct2
Distinct (%)< 0.1%
Missing12352
Missing (%)1.0%
Memory size68.6 MiB
No
602833 
Si
602297 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters2410260
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSi
2nd rowSi
3rd rowNo
4th rowSi
5th rowNo

Common Values

ValueCountFrequency (%)
No 602833
49.5%
Si 602297
49.5%
(Missing) 12352
 
1.0%

Length

2025-05-21T17:29:05.260505image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-21T17:29:05.360304image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
no 602833
50.0%
si 602297
50.0%

Most occurring characters

ValueCountFrequency (%)
N 602833
25.0%
o 602833
25.0%
S 602297
25.0%
i 602297
25.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2410260
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
N 602833
25.0%
o 602833
25.0%
S 602297
25.0%
i 602297
25.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2410260
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
N 602833
25.0%
o 602833
25.0%
S 602297
25.0%
i 602297
25.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2410260
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
N 602833
25.0%
o 602833
25.0%
S 602297
25.0%
i 602297
25.0%

ESTU_ESTADOINVESTIGACION
Categorical

Imbalance 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size75.6 MiB
PUBLICAR
1215129 
PRESENTE CON LECTURA TARDIA
 
1313
VALIDEZ OFICINA JURÍDICA
 
789
NO SE COMPROBO IDENTIDAD DEL EXAMINADO
 
251

Length

Max length38
Median length8
Mean length8.0370445
Min length8

Characters and Unicode

Total characters9784957
Distinct characters22
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPUBLICAR
2nd rowPUBLICAR
3rd rowPUBLICAR
4th rowPUBLICAR
5th rowPUBLICAR

Common Values

ValueCountFrequency (%)
PUBLICAR 1215129
99.8%
PRESENTE CON LECTURA TARDIA 1313
 
0.1%
VALIDEZ OFICINA JURÍDICA 789
 
0.1%
NO SE COMPROBO IDENTIDAD DEL EXAMINADO 251
 
< 0.1%

Length

2025-05-21T17:29:05.472584image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-21T17:29:05.626086image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
publicar 1215129
99.3%
presente 1313
 
0.1%
con 1313
 
0.1%
lectura 1313
 
0.1%
tardia 1313
 
0.1%
validez 789
 
0.1%
oficina 789
 
0.1%
jurídica 789
 
0.1%
no 251
 
< 0.1%
se 251
 
< 0.1%
Other values (4) 1004
 
0.1%

Most occurring characters

ValueCountFrequency (%)
A 1222188
12.5%
I 1220351
12.5%
R 1220108
12.5%
C 1219584
12.5%
L 1217482
12.4%
U 1217231
12.4%
P 1216693
12.4%
B 1215380
12.4%
E 7045
 
0.1%
6772
 
0.1%
Other values (12) 22123
 
0.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 9784957
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A 1222188
12.5%
I 1220351
12.5%
R 1220108
12.5%
C 1219584
12.5%
L 1217482
12.4%
U 1217231
12.4%
P 1216693
12.4%
B 1215380
12.4%
E 7045
 
0.1%
6772
 
0.1%
Other values (12) 22123
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 9784957
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A 1222188
12.5%
I 1220351
12.5%
R 1220108
12.5%
C 1219584
12.5%
L 1217482
12.4%
U 1217231
12.4%
P 1216693
12.4%
B 1215380
12.4%
E 7045
 
0.1%
6772
 
0.1%
Other values (12) 22123
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 9784957
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A 1222188
12.5%
I 1220351
12.5%
R 1220108
12.5%
C 1219584
12.5%
L 1217482
12.4%
U 1217231
12.4%
P 1216693
12.4%
B 1215380
12.4%
E 7045
 
0.1%
6772
 
0.1%
Other values (12) 22123
 
0.2%
Distinct16943
Distinct (%)1.4%
Missing1
Missing (%)< 0.1%
Memory size77.8 MiB
2025-05-21T17:29:06.259131image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters12174810
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2194 ?
Unique (%)0.2%

Sample

1st row18/09/1997
2nd row24/07/1995
3rd row18/03/1994
4th row29/06/1997
5th row23/09/1987
ValueCountFrequency (%)
17/09/1997 520
 
< 0.1%
22/09/1997 488
 
< 0.1%
03/10/1997 487
 
< 0.1%
27/12/1996 479
 
< 0.1%
19/09/1997 478
 
< 0.1%
30/09/1997 477
 
< 0.1%
05/05/1997 477
 
< 0.1%
06/06/1997 477
 
< 0.1%
20/05/1997 476
 
< 0.1%
27/09/1997 476
 
< 0.1%
Other values (16933) 1212646
99.6%
2025-05-21T17:29:06.986395image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
/ 2434962
20.0%
9 2353021
19.3%
1 2251528
18.5%
0 1831326
15.0%
2 893871
 
7.3%
8 568938
 
4.7%
7 442537
 
3.6%
6 382723
 
3.1%
3 356490
 
2.9%
5 345717
 
2.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 12174810
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
/ 2434962
20.0%
9 2353021
19.3%
1 2251528
18.5%
0 1831326
15.0%
2 893871
 
7.3%
8 568938
 
4.7%
7 442537
 
3.6%
6 382723
 
3.1%
3 356490
 
2.9%
5 345717
 
2.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 12174810
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
/ 2434962
20.0%
9 2353021
19.3%
1 2251528
18.5%
0 1831326
15.0%
2 893871
 
7.3%
8 568938
 
4.7%
7 442537
 
3.6%
6 382723
 
3.1%
3 356490
 
2.9%
5 345717
 
2.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 12174810
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
/ 2434962
20.0%
9 2353021
19.3%
1 2251528
18.5%
0 1831326
15.0%
2 893871
 
7.3%
8 568938
 
4.7%
7 442537
 
3.6%
6 382723
 
3.1%
3 356490
 
2.9%
5 345717
 
2.8%

ESTU_PAGOMATRICULAPROPIO
Categorical

Missing 

Distinct2
Distinct (%)< 0.1%
Missing12416
Missing (%)1.0%
Memory size68.6 MiB
No
674944 
Si
530122 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters2410132
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNo
2nd rowNo
3rd rowSi
4th rowNo
5th rowSi

Common Values

ValueCountFrequency (%)
No 674944
55.4%
Si 530122
43.5%
(Missing) 12416
 
1.0%

Length

2025-05-21T17:29:07.119551image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-21T17:29:07.208347image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
no 674944
56.0%
si 530122
44.0%

Most occurring characters

ValueCountFrequency (%)
N 674944
28.0%
o 674944
28.0%
S 530122
22.0%
i 530122
22.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2410132
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
N 674944
28.0%
o 674944
28.0%
S 530122
22.0%
i 530122
22.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2410132
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
N 674944
28.0%
o 674944
28.0%
S 530122
22.0%
i 530122
22.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2410132
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
N 674944
28.0%
o 674944
28.0%
S 530122
22.0%
i 530122
22.0%

ESTU_TIPODOCUMENTOSB11
Categorical

Imbalance  Missing 

Distinct6
Distinct (%)< 0.1%
Missing26035
Missing (%)2.1%
Memory size68.6 MiB
TI
827847 
CC
342290 
CR
 
19075
CE
 
1543
PE
 
589

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters2382894
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCC
2nd rowTI
3rd rowCC
4th rowTI
5th rowTI

Common Values

ValueCountFrequency (%)
TI 827847
68.0%
CC 342290
28.1%
CR 19075
 
1.6%
CE 1543
 
0.1%
PE 589
 
< 0.1%
PC 103
 
< 0.1%
(Missing) 26035
 
2.1%

Length

2025-05-21T17:29:07.356263image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-21T17:29:07.526842image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
ti 827847
69.5%
cc 342290
28.7%
cr 19075
 
1.6%
ce 1543
 
0.1%
pe 589
 
< 0.1%
pc 103
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
T 827847
34.7%
I 827847
34.7%
C 705301
29.6%
R 19075
 
0.8%
E 2132
 
0.1%
P 692
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2382894
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
T 827847
34.7%
I 827847
34.7%
C 705301
29.6%
R 19075
 
0.8%
E 2132
 
0.1%
P 692
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2382894
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
T 827847
34.7%
I 827847
34.7%
C 705301
29.6%
R 19075
 
0.8%
E 2132
 
0.1%
P 692
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2382894
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
T 827847
34.7%
I 827847
34.7%
C 705301
29.6%
R 19075
 
0.8%
E 2132
 
0.1%
P 692
 
< 0.1%

FAMI_EDUCACIONPADRE
Categorical

Missing 

Distinct12
Distinct (%)< 0.1%
Missing41222
Missing (%)3.4%
Memory size113.8 MiB
Secundaria (Bachillerato) completa
227061 
Primaria incompleta
220650 
Educación profesional completa
146361 
Secundaria (Bachillerato) incompleta
123295 
Técnica o tecnológica completa
112220 
Other values (7)
346673 

Length

Max length36
Median length32
Mean length25.414726
Min length7

Characters and Unicode

Total characters29894326
Distinct characters30
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPrimaria completa
2nd rowPrimaria incompleta
3rd rowNo Aplica
4th rowPostgrado
5th rowEducación profesional completa

Common Values

ValueCountFrequency (%)
Secundaria (Bachillerato) completa 227061
18.7%
Primaria incompleta 220650
18.1%
Educación profesional completa 146361
12.0%
Secundaria (Bachillerato) incompleta 123295
10.1%
Técnica o tecnológica completa 112220
9.2%
Primaria completa 98080
8.1%
Postgrado 78145
 
6.4%
Educación profesional incompleta 48714
 
4.0%
Técnica o tecnológica incompleta 38905
 
3.2%
Ninguno 37038
 
3.0%
Other values (2) 45791
 
3.8%
(Missing) 41222
 
3.4%

Length

2025-05-21T17:29:07.764767image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
completa 583722
18.9%
incompleta 431564
14.0%
secundaria 350356
11.4%
bachillerato 350356
11.4%
primaria 318730
10.3%
educación 195075
 
6.3%
profesional 195075
 
6.3%
técnica 151125
 
4.9%
o 151125
 
4.9%
tecnológica 151125
 
4.9%
Other values (5) 206765
 
6.7%

Most occurring characters

ValueCountFrequency (%)
a 3870506
12.9%
c 2726436
 
9.1%
i 2514962
 
8.4%
o 2297161
 
7.7%
e 2092201
 
7.0%
l 2077986
 
7.0%
1908758
 
6.4%
r 1611392
 
5.4%
t 1594912
 
5.3%
n 1548396
 
5.2%
Other values (20) 7651616
25.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 29894326
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 3870506
12.9%
c 2726436
 
9.1%
i 2514962
 
8.4%
o 2297161
 
7.7%
e 2092201
 
7.0%
l 2077986
 
7.0%
1908758
 
6.4%
r 1611392
 
5.4%
t 1594912
 
5.3%
n 1548396
 
5.2%
Other values (20) 7651616
25.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 29894326
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 3870506
12.9%
c 2726436
 
9.1%
i 2514962
 
8.4%
o 2297161
 
7.7%
e 2092201
 
7.0%
l 2077986
 
7.0%
1908758
 
6.4%
r 1611392
 
5.4%
t 1594912
 
5.3%
n 1548396
 
5.2%
Other values (20) 7651616
25.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 29894326
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 3870506
12.9%
c 2726436
 
9.1%
i 2514962
 
8.4%
o 2297161
 
7.7%
e 2092201
 
7.0%
l 2077986
 
7.0%
1908758
 
6.4%
r 1611392
 
5.4%
t 1594912
 
5.3%
n 1548396
 
5.2%
Other values (20) 7651616
25.6%

FAMI_TIENEAUTOMOVIL
Categorical

Missing 

Distinct2
Distinct (%)< 0.1%
Missing76214
Missing (%)6.3%
Memory size68.9 MiB
No
730919 
Si
410349 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters2282536
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSi
2nd rowNo
3rd rowNo
4th rowNo
5th rowNo

Common Values

ValueCountFrequency (%)
No 730919
60.0%
Si 410349
33.7%
(Missing) 76214
 
6.3%

Length

2025-05-21T17:29:07.941997image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-21T17:29:08.025578image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
no 730919
64.0%
si 410349
36.0%

Most occurring characters

ValueCountFrequency (%)
N 730919
32.0%
o 730919
32.0%
S 410349
18.0%
i 410349
18.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2282536
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
N 730919
32.0%
o 730919
32.0%
S 410349
18.0%
i 410349
18.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2282536
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
N 730919
32.0%
o 730919
32.0%
S 410349
18.0%
i 410349
18.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2282536
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
N 730919
32.0%
o 730919
32.0%
S 410349
18.0%
i 410349
18.0%

FAMI_TIENELAVADORA
Categorical

Missing 

Distinct2
Distinct (%)< 0.1%
Missing67718
Missing (%)5.6%
Memory size68.8 MiB
Si
989258 
No
160506 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters2299528
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSi
2nd rowSi
3rd rowSi
4th rowSi
5th rowSi

Common Values

ValueCountFrequency (%)
Si 989258
81.3%
No 160506
 
13.2%
(Missing) 67718
 
5.6%

Length

2025-05-21T17:29:08.126483image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-21T17:29:08.225706image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
si 989258
86.0%
no 160506
 
14.0%

Most occurring characters

ValueCountFrequency (%)
S 989258
43.0%
i 989258
43.0%
N 160506
 
7.0%
o 160506
 
7.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2299528
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
S 989258
43.0%
i 989258
43.0%
N 160506
 
7.0%
o 160506
 
7.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2299528
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
S 989258
43.0%
i 989258
43.0%
N 160506
 
7.0%
o 160506
 
7.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2299528
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
S 989258
43.0%
i 989258
43.0%
N 160506
 
7.0%
o 160506
 
7.0%

FAMI_ESTRATOVIVIENDA
Categorical

Missing 

Distinct7
Distinct (%)< 0.1%
Missing55103
Missing (%)4.5%
Memory size76.5 MiB
Estrato 2
409822 
Estrato 3
366054 
Estrato 1
205875 
Estrato 4
112573 
Estrato 5
 
40673
Other values (2)
 
27382

Length

Max length11
Median length9
Mean length9.009761
Min length9

Characters and Unicode

Total characters10472757
Distinct characters16
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowEstrato 2
2nd rowEstrato 2
3rd rowEstrato 2
4th rowEstrato 2
5th rowEstrato 3

Common Values

ValueCountFrequency (%)
Estrato 2 409822
33.7%
Estrato 3 366054
30.1%
Estrato 1 205875
16.9%
Estrato 4 112573
 
9.2%
Estrato 5 40673
 
3.3%
Estrato 6 21709
 
1.8%
Sin Estrato 5673
 
0.5%
(Missing) 55103
 
4.5%

Length

2025-05-21T17:29:08.378901image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-21T17:29:08.506252image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
estrato 1162379
50.0%
2 409822
 
17.6%
3 366054
 
15.7%
1 205875
 
8.9%
4 112573
 
4.8%
5 40673
 
1.7%
6 21709
 
0.9%
sin 5673
 
0.2%

Most occurring characters

ValueCountFrequency (%)
t 2324758
22.2%
E 1162379
11.1%
s 1162379
11.1%
r 1162379
11.1%
a 1162379
11.1%
o 1162379
11.1%
1162379
11.1%
2 409822
 
3.9%
3 366054
 
3.5%
1 205875
 
2.0%
Other values (6) 191974
 
1.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 10472757
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
t 2324758
22.2%
E 1162379
11.1%
s 1162379
11.1%
r 1162379
11.1%
a 1162379
11.1%
o 1162379
11.1%
1162379
11.1%
2 409822
 
3.9%
3 366054
 
3.5%
1 205875
 
2.0%
Other values (6) 191974
 
1.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 10472757
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
t 2324758
22.2%
E 1162379
11.1%
s 1162379
11.1%
r 1162379
11.1%
a 1162379
11.1%
o 1162379
11.1%
1162379
11.1%
2 409822
 
3.9%
3 366054
 
3.5%
1 205875
 
2.0%
Other values (6) 191974
 
1.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 10472757
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
t 2324758
22.2%
E 1162379
11.1%
s 1162379
11.1%
r 1162379
11.1%
a 1162379
11.1%
o 1162379
11.1%
1162379
11.1%
2 409822
 
3.9%
3 366054
 
3.5%
1 205875
 
2.0%
Other values (6) 191974
 
1.8%

FAMI_TIENECOMPUTADOR
Categorical

Imbalance  Missing 

Distinct2
Distinct (%)< 0.1%
Missing64913
Missing (%)5.3%
Memory size68.8 MiB
Si
1045989 
No
106580 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters2305138
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSi
2nd rowSi
3rd rowSi
4th rowSi
5th rowSi

Common Values

ValueCountFrequency (%)
Si 1045989
85.9%
No 106580
 
8.8%
(Missing) 64913
 
5.3%

Length

2025-05-21T17:29:08.691795image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-21T17:29:08.834564image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
si 1045989
90.8%
no 106580
 
9.2%

Most occurring characters

ValueCountFrequency (%)
S 1045989
45.4%
i 1045989
45.4%
N 106580
 
4.6%
o 106580
 
4.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2305138
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
S 1045989
45.4%
i 1045989
45.4%
N 106580
 
4.6%
o 106580
 
4.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2305138
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
S 1045989
45.4%
i 1045989
45.4%
N 106580
 
4.6%
o 106580
 
4.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2305138
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
S 1045989
45.4%
i 1045989
45.4%
N 106580
 
4.6%
o 106580
 
4.6%

FAMI_TIENEINTERNET
Categorical

Missing 

Distinct2
Distinct (%)< 0.1%
Missing47504
Missing (%)3.9%
Memory size68.7 MiB
Si
1040839 
No
129139 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters2339956
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSi
2nd rowSi
3rd rowSi
4th rowSi
5th rowSi

Common Values

ValueCountFrequency (%)
Si 1040839
85.5%
No 129139
 
10.6%
(Missing) 47504
 
3.9%

Length

2025-05-21T17:29:09.022538image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-21T17:29:09.122372image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
si 1040839
89.0%
no 129139
 
11.0%

Most occurring characters

ValueCountFrequency (%)
S 1040839
44.5%
i 1040839
44.5%
N 129139
 
5.5%
o 129139
 
5.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2339956
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
S 1040839
44.5%
i 1040839
44.5%
N 129139
 
5.5%
o 129139
 
5.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2339956
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
S 1040839
44.5%
i 1040839
44.5%
N 129139
 
5.5%
o 129139
 
5.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2339956
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
S 1040839
44.5%
i 1040839
44.5%
N 129139
 
5.5%
o 129139
 
5.5%

FAMI_EDUCACIONMADRE
Categorical

Missing 

Distinct12
Distinct (%)< 0.1%
Missing42018
Missing (%)3.5%
Memory size118.3 MiB
Secundaria (Bachillerato) completa
250124 
Primaria incompleta
172886 
Técnica o tecnológica completa
160854 
Educación profesional completa
152001 
Secundaria (Bachillerato) incompleta
139456 
Other values (7)
300143 

Length

Max length36
Median length32
Mean length26.891574
Min length7

Characters and Unicode

Total characters31610077
Distinct characters30
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSecundaria (Bachillerato) incompleta
2nd rowSecundaria (Bachillerato) incompleta
3rd rowNo sabe
4th rowTécnica o tecnológica completa
5th rowEducación profesional completa

Common Values

ValueCountFrequency (%)
Secundaria (Bachillerato) completa 250124
20.5%
Primaria incompleta 172886
14.2%
Técnica o tecnológica completa 160854
13.2%
Educación profesional completa 152001
12.5%
Secundaria (Bachillerato) incompleta 139456
11.5%
Primaria completa 98295
 
8.1%
Postgrado 82756
 
6.8%
Técnica o tecnológica incompleta 47694
 
3.9%
Educación profesional incompleta 38676
 
3.2%
Ninguno 24348
 
2.0%
Other values (2) 8374
 
0.7%
(Missing) 42018
 
3.5%

Length

2025-05-21T17:29:09.308412image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
completa 661274
20.4%
incompleta 398712
12.3%
secundaria 389580
12.0%
bachillerato 389580
12.0%
primaria 271181
8.4%
técnica 208548
 
6.4%
o 208548
 
6.4%
tecnológica 208548
 
6.4%
educación 190677
 
5.9%
profesional 190677
 
5.9%
Other values (5) 123852
 
3.8%

Most occurring characters

ValueCountFrequency (%)
a 4050248
12.8%
c 3057816
 
9.7%
i 2546156
 
8.1%
o 2446250
 
7.7%
e 2243621
 
7.1%
l 2241495
 
7.1%
2065713
 
6.5%
t 1740870
 
5.5%
n 1635438
 
5.2%
r 1594955
 
5.0%
Other values (20) 7987515
25.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 31610077
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 4050248
12.8%
c 3057816
 
9.7%
i 2546156
 
8.1%
o 2446250
 
7.7%
e 2243621
 
7.1%
l 2241495
 
7.1%
2065713
 
6.5%
t 1740870
 
5.5%
n 1635438
 
5.2%
r 1594955
 
5.0%
Other values (20) 7987515
25.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 31610077
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 4050248
12.8%
c 3057816
 
9.7%
i 2546156
 
8.1%
o 2446250
 
7.7%
e 2243621
 
7.1%
l 2241495
 
7.1%
2065713
 
6.5%
t 1740870
 
5.5%
n 1635438
 
5.2%
r 1594955
 
5.0%
Other values (20) 7987515
25.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 31610077
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 4050248
12.8%
c 3057816
 
9.7%
i 2546156
 
8.1%
o 2446250
 
7.7%
e 2243621
 
7.1%
l 2241495
 
7.1%
2065713
 
6.5%
t 1740870
 
5.5%
n 1635438
 
5.2%
r 1594955
 
5.0%
Other values (20) 7987515
25.3%

INST_ORIGEN
Categorical

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size129.1 MiB
NO OFICIAL - CORPORACIÓN
428157 
NO OFICIAL - FUNDACIÓN
382810 
OFICIAL NACIONAL
193215 
OFICIAL DEPARTAMENTAL
186150 
OFICIAL MUNICIPAL
 
26285

Length

Max length24
Median length22
Mean length21.486037
Min length16

Characters and Unicode

Total characters26158863
Distinct characters19
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNO OFICIAL - CORPORACIÓN
2nd rowOFICIAL DEPARTAMENTAL
3rd rowNO OFICIAL - CORPORACIÓN
4th rowOFICIAL DEPARTAMENTAL
5th rowOFICIAL DEPARTAMENTAL

Common Values

ValueCountFrequency (%)
NO OFICIAL - CORPORACIÓN 428157
35.2%
NO OFICIAL - FUNDACIÓN 382810
31.4%
OFICIAL NACIONAL 193215
15.9%
OFICIAL DEPARTAMENTAL 186150
15.3%
OFICIAL MUNICIPAL 26285
 
2.2%
REGIMEN ESPECIAL 865
 
0.1%

Length

2025-05-21T17:29:09.559554image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-21T17:29:09.722513image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
oficial 1216617
30.0%
no 810967
20.0%
810967
20.0%
corporación 428157
 
10.6%
fundación 382810
 
9.4%
nacional 193215
 
4.8%
departamental 186150
 
4.6%
municipal 26285
 
0.6%
regimen 865
 
< 0.1%
especial 865
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
I 3491716
13.3%
O 3077113
11.8%
A 2999614
11.5%
2839416
10.9%
C 2676106
10.2%
N 2604474
10.0%
L 1623132
6.2%
F 1599427
6.1%
R 1043329
 
4.0%
- 810967
 
3.1%
Other values (9) 3393569
13.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 26158863
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
I 3491716
13.3%
O 3077113
11.8%
A 2999614
11.5%
2839416
10.9%
C 2676106
10.2%
N 2604474
10.0%
L 1623132
6.2%
F 1599427
6.1%
R 1043329
 
4.0%
- 810967
 
3.1%
Other values (9) 3393569
13.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 26158863
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
I 3491716
13.3%
O 3077113
11.8%
A 2999614
11.5%
2839416
10.9%
C 2676106
10.2%
N 2604474
10.0%
L 1623132
6.2%
F 1599427
6.1%
R 1043329
 
4.0%
- 810967
 
3.1%
Other values (9) 3393569
13.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 26158863
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
I 3491716
13.3%
O 3077113
11.8%
A 2999614
11.5%
2839416
10.9%
C 2676106
10.2%
N 2604474
10.0%
L 1623132
6.2%
F 1599427
6.1%
R 1043329
 
4.0%
- 810967
 
3.1%
Other values (9) 3393569
13.0%

MOD_RAZONA_CUANTITAT_PUNT
Real number (ℝ)

High correlation 

Distinct179
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean146.77354
Minimum0
Maximum300
Zeros432
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size9.3 MiB
2025-05-21T17:29:10.172553image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile95
Q1123
median147
Q3169
95-th percentile200
Maximum300
Range300
Interquartile range (IQR)46

Descriptive statistics

Standard deviation32.273339
Coefficient of variation (CV)0.21988527
Kurtosis0.1869979
Mean146.77354
Median Absolute Deviation (MAD)23
Skewness0.17534555
Sum1.7869414 × 108
Variance1041.5684
MonotonicityNot monotonic
2025-05-21T17:29:10.622442image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
148 14429
 
1.2%
145 14420
 
1.2%
144 14364
 
1.2%
150 14361
 
1.2%
146 14356
 
1.2%
152 14348
 
1.2%
141 14287
 
1.2%
147 14257
 
1.2%
153 14228
 
1.2%
143 14208
 
1.2%
Other values (169) 1074224
88.2%
ValueCountFrequency (%)
0 432
< 0.1%
61 4
 
< 0.1%
62 4
 
< 0.1%
63 3
 
< 0.1%
64 41
 
< 0.1%
65 76
 
< 0.1%
66 121
 
< 0.1%
67 204
< 0.1%
68 225
< 0.1%
69 304
< 0.1%
ValueCountFrequency (%)
300 1734
0.1%
237 23
 
< 0.1%
236 36
 
< 0.1%
235 99
 
< 0.1%
234 112
 
< 0.1%
233 369
 
< 0.1%
232 249
 
< 0.1%
231 259
 
< 0.1%
230 365
 
< 0.1%
229 558
 
< 0.1%

MOD_COMUNI_ESCRITA_PUNT
Real number (ℝ)

High correlation  Zeros 

Distinct199
Distinct (%)< 0.1%
Missing7466
Missing (%)0.6%
Infinite0
Infinite (%)0.0%
Mean139.10814
Minimum0
Maximum300
Zeros48006
Zeros (%)3.9%
Negative0
Negative (%)0.0%
Memory size9.3 MiB
2025-05-21T17:29:10.928933image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile80
Q1124
median140
Q3164
95-th percentile191
Maximum300
Range300
Interquartile range (IQR)40

Descriptive statistics

Standard deviation42.175146
Coefficient of variation (CV)0.30318244
Kurtosis4.1691207
Mean139.10814
Median Absolute Deviation (MAD)20
Skewness-0.7996073
Sum1.6832307 × 108
Variance1778.7429
MonotonicityNot monotonic
2025-05-21T17:29:11.639045image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 48006
 
3.9%
133 20316
 
1.7%
131 19817
 
1.6%
135 19736
 
1.6%
134 19376
 
1.6%
136 19361
 
1.6%
132 19241
 
1.6%
129 19207
 
1.6%
137 19077
 
1.6%
130 19028
 
1.6%
Other values (189) 986851
81.1%
ValueCountFrequency (%)
0 48006
3.9%
47 7
 
< 0.1%
48 2
 
< 0.1%
49 50
 
< 0.1%
53 13
 
< 0.1%
54 11
 
< 0.1%
55 71
 
< 0.1%
56 71
 
< 0.1%
57 32
 
< 0.1%
58 65
 
< 0.1%
ValueCountFrequency (%)
300 11032
0.9%
247 1
 
< 0.1%
245 3
 
< 0.1%
244 3
 
< 0.1%
243 5
 
< 0.1%
242 5
 
< 0.1%
241 8
 
< 0.1%
240 8
 
< 0.1%
239 9
 
< 0.1%
238 6
 
< 0.1%

MOD_COMUNI_ESCRITA_DESEM
Categorical

High correlation  Missing 

Distinct4
Distinct (%)< 0.1%
Missing55472
Missing (%)4.6%
Memory size69.9 MiB
2.0
516323 
3.0
373475 
1.0
170017 
4.0
102195 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters3486030
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3.0
2nd row3.0
3rd row1.0
4th row3.0
5th row2.0

Common Values

ValueCountFrequency (%)
2.0 516323
42.4%
3.0 373475
30.7%
1.0 170017
 
14.0%
4.0 102195
 
8.4%
(Missing) 55472
 
4.6%

Length

2025-05-21T17:29:11.884317image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-21T17:29:12.088796image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
2.0 516323
44.4%
3.0 373475
32.1%
1.0 170017
 
14.6%
4.0 102195
 
8.8%

Most occurring characters

ValueCountFrequency (%)
. 1162010
33.3%
0 1162010
33.3%
2 516323
14.8%
3 373475
 
10.7%
1 170017
 
4.9%
4 102195
 
2.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3486030
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
. 1162010
33.3%
0 1162010
33.3%
2 516323
14.8%
3 373475
 
10.7%
1 170017
 
4.9%
4 102195
 
2.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3486030
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
. 1162010
33.3%
0 1162010
33.3%
2 516323
14.8%
3 373475
 
10.7%
1 170017
 
4.9%
4 102195
 
2.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3486030
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
. 1162010
33.3%
0 1162010
33.3%
2 516323
14.8%
3 373475
 
10.7%
1 170017
 
4.9%
4 102195
 
2.9%

MOD_INGLES_DESEM
Categorical

High correlation 

Distinct5
Distinct (%)< 0.1%
Missing123
Missing (%)< 0.1%
Memory size68.6 MiB
A2
460586 
A1
260569 
B1
259133 
-A1
118976 
B2
118095 

Length

Max length3
Median length2
Mean length2.0977329
Min length2

Characters and Unicode

Total characters2553694
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowA2
2nd rowB1
3rd rowA1
4th row-A1
5th rowA1

Common Values

ValueCountFrequency (%)
A2 460586
37.8%
A1 260569
21.4%
B1 259133
21.3%
-A1 118976
 
9.8%
B2 118095
 
9.7%
(Missing) 123
 
< 0.1%

Length

2025-05-21T17:29:12.235454image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-21T17:29:12.347266image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
a2 460586
37.8%
a1 379545
31.2%
b1 259133
21.3%
b2 118095
 
9.7%

Most occurring characters

ValueCountFrequency (%)
A 840131
32.9%
1 638678
25.0%
2 578681
22.7%
B 377228
14.8%
- 118976
 
4.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2553694
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A 840131
32.9%
1 638678
25.0%
2 578681
22.7%
B 377228
14.8%
- 118976
 
4.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2553694
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A 840131
32.9%
1 638678
25.0%
2 578681
22.7%
B 377228
14.8%
- 118976
 
4.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2553694
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A 840131
32.9%
1 638678
25.0%
2 578681
22.7%
B 377228
14.8%
- 118976
 
4.7%

MOD_LECTURA_CRITICA_PUNT
Real number (ℝ)

High correlation 

Distinct188
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean149.40504
Minimum0
Maximum300
Zeros1455
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size9.3 MiB
2025-05-21T17:29:12.558427image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile100
Q1127
median149
Q3172
95-th percentile200
Maximum300
Range300
Interquartile range (IQR)45

Descriptive statistics

Standard deviation31.144506
Coefficient of variation (CV)0.20845686
Kurtosis0.21764416
Mean149.40504
Median Absolute Deviation (MAD)22
Skewness-0.045038128
Sum1.8189795 × 108
Variance969.98026
MonotonicityNot monotonic
2025-05-21T17:29:12.804374image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
150 14336
 
1.2%
153 14335
 
1.2%
151 14314
 
1.2%
146 14302
 
1.2%
147 14295
 
1.2%
155 14280
 
1.2%
149 14278
 
1.2%
148 14260
 
1.2%
154 14249
 
1.2%
144 14237
 
1.2%
Other values (178) 1074596
88.3%
ValueCountFrequency (%)
0 1455
0.1%
54 6
 
< 0.1%
55 7
 
< 0.1%
56 5
 
< 0.1%
57 9
 
< 0.1%
58 8
 
< 0.1%
59 38
 
< 0.1%
60 44
 
< 0.1%
61 48
 
< 0.1%
62 101
 
< 0.1%
ValueCountFrequency (%)
300 624
0.1%
239 7
 
< 0.1%
238 20
 
< 0.1%
237 39
 
< 0.1%
236 34
 
< 0.1%
235 109
 
< 0.1%
234 82
 
< 0.1%
233 128
 
< 0.1%
232 219
 
< 0.1%
231 273
< 0.1%

MOD_INGLES_PUNT
Real number (ℝ)

High correlation 

Distinct166
Distinct (%)< 0.1%
Missing123
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean153.56511
Minimum0
Maximum300
Zeros7690
Zeros (%)0.6%
Negative0
Negative (%)0.0%
Memory size9.3 MiB
2025-05-21T17:29:13.044620image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile108
Q1132
median151
Q3175
95-th percentile208
Maximum300
Range300
Interquartile range (IQR)43

Descriptive statistics

Standard deviation33.662837
Coefficient of variation (CV)0.21920889
Kurtosis3.1527451
Mean153.56511
Median Absolute Deviation (MAD)21
Skewness0.022802667
Sum1.8694387 × 108
Variance1133.1866
MonotonicityNot monotonic
2025-05-21T17:29:13.342180image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
138 16461
 
1.4%
139 16409
 
1.3%
140 16368
 
1.3%
141 16290
 
1.3%
142 16227
 
1.3%
137 16130
 
1.3%
136 16049
 
1.3%
135 16033
 
1.3%
134 15874
 
1.3%
132 15796
 
1.3%
Other values (156) 1055722
86.7%
ValueCountFrequency (%)
0 7690
0.6%
72 6
 
< 0.1%
73 32
 
< 0.1%
74 103
 
< 0.1%
75 99
 
< 0.1%
76 157
 
< 0.1%
77 261
 
< 0.1%
78 492
 
< 0.1%
79 832
 
0.1%
80 791
 
0.1%
ValueCountFrequency (%)
300 6536
0.5%
235 13
 
< 0.1%
234 19
 
< 0.1%
233 109
 
< 0.1%
232 224
 
< 0.1%
231 329
 
< 0.1%
230 346
 
< 0.1%
229 515
 
< 0.1%
228 586
 
< 0.1%
227 605
 
< 0.1%

MOD_COMPETEN_CIUDADA_PUNT
Real number (ℝ)

High correlation 

Distinct183
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean144.86761
Minimum0
Maximum300
Zeros3147
Zeros (%)0.3%
Negative0
Negative (%)0.0%
Memory size9.3 MiB
2025-05-21T17:29:13.584848image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile89
Q1121
median146
Q3169
95-th percentile198
Maximum300
Range300
Interquartile range (IQR)48

Descriptive statistics

Standard deviation33.854748
Coefficient of variation (CV)0.23369439
Kurtosis0.40316372
Mean144.86761
Median Absolute Deviation (MAD)24
Skewness-0.15421906
Sum1.7637371 × 108
Variance1146.144
MonotonicityNot monotonic
2025-05-21T17:29:13.790679image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
150 13759
 
1.1%
153 13671
 
1.1%
154 13638
 
1.1%
152 13638
 
1.1%
155 13622
 
1.1%
157 13602
 
1.1%
145 13547
 
1.1%
148 13516
 
1.1%
158 13509
 
1.1%
149 13495
 
1.1%
Other values (173) 1081485
88.8%
ValueCountFrequency (%)
0 3147
0.3%
57 5
 
< 0.1%
58 25
 
< 0.1%
59 65
 
< 0.1%
60 130
 
< 0.1%
61 235
 
< 0.1%
62 305
 
< 0.1%
63 367
 
< 0.1%
64 409
 
< 0.1%
65 538
 
< 0.1%
ValueCountFrequency (%)
300 1179
0.1%
237 18
 
< 0.1%
236 30
 
< 0.1%
235 23
 
< 0.1%
234 56
 
< 0.1%
233 62
 
< 0.1%
232 110
 
< 0.1%
231 101
 
< 0.1%
230 154
 
< 0.1%
229 178
 
< 0.1%

Interactions

2025-05-21T17:25:35.341218image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-21T17:23:20.875858image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-21T17:23:29.411679image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-21T17:23:38.173509image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-21T17:23:47.189549image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-21T17:23:53.512400image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-21T17:23:59.773177image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-21T17:24:09.958233image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-21T17:24:20.751634image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-21T17:24:30.683485image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-21T17:24:40.286623image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-21T17:24:51.033391image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-21T17:24:59.957606image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-21T17:25:09.096900image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-21T17:25:17.913027image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-21T17:25:26.675907image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-05-21T17:23:21.461849image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-05-21T17:25:09.682119image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-21T17:25:18.568820image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-05-21T17:24:17.568909image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-21T17:24:27.283721image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-21T17:24:37.106705image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-21T17:24:47.441197image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-21T17:24:57.149537image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-21T17:25:06.378576image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-21T17:25:15.131928image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-21T17:25:23.993016image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-21T17:25:32.618542image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-21T17:25:42.023477image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-21T17:23:27.208349image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-21T17:23:35.943255image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-21T17:23:45.071759image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-21T17:23:51.723916image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-21T17:23:57.971055image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-21T17:24:07.515078image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-21T17:24:18.288986image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-21T17:24:27.878303image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-21T17:24:37.712076image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-21T17:24:48.117900image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-21T17:24:57.776448image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-21T17:25:06.895058image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-21T17:25:15.659754image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-21T17:25:24.551803image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-21T17:25:33.125132image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-21T17:25:42.617322image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-21T17:23:27.729935image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-21T17:23:36.490574image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-21T17:23:45.622503image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-21T17:23:52.060772image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-21T17:23:58.357252image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-21T17:24:08.141285image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-21T17:24:18.929900image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-21T17:24:28.817027image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-21T17:24:38.282770image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-21T17:24:48.787839image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-21T17:24:58.316851image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-05-21T17:25:25.075253image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-21T17:25:33.647563image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-21T17:25:43.173479image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-21T17:23:28.324997image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-05-21T17:24:19.585339image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-21T17:24:29.449844image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-05-21T17:24:50.347195image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-21T17:24:59.495195image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-05-21T17:25:17.377301image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-21T17:25:26.176083image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-21T17:25:34.742145image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-05-21T17:29:14.122198image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ESTU_CODDANE_COLE_TERMINOESTU_COD_COLE_MCPIO_TERMINOESTU_COD_DEPTO_PRESENTACIONESTU_COD_MCPIO_PRESENTACIONESTU_COD_RESIDE_DEPTOESTU_COD_RESIDE_MCPIOESTU_ESTADOINVESTIGACIONESTU_GENEROESTU_HORASSEMANATRABAJAESTU_INST_CODMUNICIPIOESTU_INST_DEPARTAMENTOESTU_METODO_PRGMESTU_PAGOMATRICULABECAESTU_PAGOMATRICULACREDITOESTU_PAGOMATRICULAPADRESESTU_PAGOMATRICULAPROPIOESTU_PRGM_CODMUNICIPIOESTU_PRGM_DEPARTAMENTOESTU_PRIVADO_LIBERTADESTU_SNIES_PRGMACADEMICOESTU_TIPODOCUMENTOESTU_TIPODOCUMENTOSB11ESTU_VALORMATRICULAUNIVERSIDADFAMI_EDUCACIONMADREFAMI_EDUCACIONPADREFAMI_ESTRATOVIVIENDAFAMI_TIENEAUTOMOVILFAMI_TIENECOMPUTADORFAMI_TIENEINTERNETFAMI_TIENELAVADORAINST_CARACTER_ACADEMICOINST_COD_INSTITUCIONINST_ORIGENMOD_COMPETEN_CIUDADA_PUNTMOD_COMUNI_ESCRITA_DESEMMOD_COMUNI_ESCRITA_PUNTMOD_INGLES_DESEMMOD_INGLES_PUNTMOD_LECTURA_CRITICA_PUNTMOD_RAZONA_CUANTITAT_PUNTPERIODO
ESTU_CODDANE_COLE_TERMINO1.0000.3790.1760.1760.3100.3070.0170.0430.0560.2410.1920.1040.0630.0180.1900.1410.2630.2110.010-0.1110.0060.0240.1250.1030.1010.1480.2660.0950.1140.1080.079-0.0870.0940.0800.0510.0460.1420.1830.0800.0810.009
ESTU_COD_COLE_MCPIO_TERMINO0.3791.0000.5320.5320.8420.8410.0330.0130.0660.6320.5170.1130.0430.0440.0450.0470.6900.5810.0010.0090.0060.0240.0950.0370.0430.1130.1040.1100.1790.1980.109-0.0720.183-0.0800.040-0.0520.088-0.127-0.089-0.0680.000
ESTU_COD_DEPTO_PRESENTACION0.1760.5321.0001.0000.6390.6380.0470.0330.0420.5210.4760.1160.0600.0310.0440.0420.5640.5400.0030.0180.0080.0210.0910.0430.0430.0930.0900.1880.2250.1270.084-0.0720.135-0.1130.0400.0120.129-0.169-0.093-0.073-0.187
ESTU_COD_MCPIO_PRESENTACION0.1760.5321.0001.0000.6390.6380.0470.0330.0420.5210.4760.1160.0600.0310.0440.0420.5640.5400.0030.0190.0080.0210.0910.0430.0430.0930.0900.1880.2250.1270.084-0.0720.135-0.1140.0400.0120.129-0.170-0.094-0.074-0.187
ESTU_COD_RESIDE_DEPTO0.3100.8420.6390.6391.0000.9990.0000.0010.0050.7150.0120.0270.0010.0020.0020.0040.7860.0130.0000.0340.0970.0210.0020.0060.0060.0420.0090.0030.0050.0050.001-0.0760.011-0.1000.001-0.0650.018-0.151-0.109-0.0870.015
ESTU_COD_RESIDE_MCPIO0.3070.8410.6380.6380.9991.0000.0310.0220.0520.7140.6200.1300.1150.0370.0590.0560.7850.7000.0000.0360.0170.0270.1060.0440.0480.1210.1020.1300.1670.1690.114-0.0750.175-0.1020.049-0.0670.094-0.155-0.112-0.0890.016
ESTU_ESTADOINVESTIGACION0.0170.0330.0470.0470.0000.0311.0000.0020.0030.0320.0600.0090.0050.0030.0050.0020.0280.0610.0000.0050.0000.0030.0110.0070.0050.0060.0040.0050.0090.0060.0040.0080.0060.0100.0070.0090.0120.0100.0110.0070.044
ESTU_GENERO0.0430.0130.0330.0330.0010.0220.0021.0000.0670.0160.0400.0980.0000.0230.0200.0500.0120.0330.0070.0810.0090.0620.0630.0660.0680.0570.0630.0140.0140.0080.0740.0850.0680.1050.0330.0360.1000.1020.0600.2290.010
ESTU_HORASSEMANATRABAJA0.0560.0660.0420.0420.0050.0520.0030.0671.0000.0800.0990.1510.0400.0530.2820.3320.0820.1020.0020.0810.0100.0350.1380.0980.0920.0750.0810.0380.0550.0550.1260.1200.0630.0670.0470.0430.0900.0940.0770.0640.033
ESTU_INST_CODMUNICIPIO0.2410.6320.5210.5210.7150.7140.0320.0160.0801.0001.0000.1510.0880.0950.1210.1030.9150.8880.004-0.0310.0080.0140.1620.0250.0280.1070.0950.1350.1650.1700.210-0.2380.285-0.0390.023-0.0240.054-0.060-0.034-0.0180.008
ESTU_INST_DEPARTAMENTO0.1920.5170.4760.4760.0120.6200.0600.0400.0991.0001.0000.2590.1730.1480.1430.1650.8090.8920.0050.1910.0140.0410.2350.0480.0490.1640.1480.2170.2240.1990.2840.2690.3770.0550.0520.0330.0950.0800.0540.0690.024
ESTU_METODO_PRGM0.1040.1130.1160.1160.0270.1300.0090.0980.1510.1510.2591.0000.0400.0090.3000.3050.1580.2460.0120.3900.0130.0810.3800.1980.1780.1250.1150.0510.0820.0680.2490.3290.2290.1390.0800.0820.1990.2070.1610.1590.050
ESTU_PAGOMATRICULABECA0.0630.0430.0600.0600.0010.1150.0050.0000.0400.0880.1730.0401.0000.0890.1110.0740.0670.1660.0000.0280.0070.0380.1990.0410.0470.1220.0920.0500.0500.0590.0800.1060.1670.0790.0420.0450.0620.0420.0880.0830.102
ESTU_PAGOMATRICULACREDITO0.0180.0440.0310.0310.0020.0370.0030.0230.0530.0950.1480.0090.0891.0000.1480.1040.0710.1370.0010.0300.0180.0200.2720.0350.0370.0550.0470.0150.0050.0060.0870.1120.2800.0490.0200.0210.0660.0640.0540.0600.031
ESTU_PAGOMATRICULAPADRES0.1900.0450.0440.0440.0020.0590.0050.0200.2820.1210.1430.3000.1110.1481.0000.3650.1210.1380.0000.1910.0120.0850.2730.3200.2870.2120.1870.0600.0550.0520.2090.2360.0490.0950.0650.0690.2060.2170.1240.1080.044
ESTU_PAGOMATRICULAPROPIO0.1410.0470.0420.0420.0040.0560.0020.0500.3320.1030.1650.3050.0740.1040.3651.0000.1040.1640.0000.2030.0120.1140.3400.2720.2420.1460.1030.0200.0230.0220.2270.2480.0620.1440.0990.1010.2270.2300.1790.1380.013
ESTU_PRGM_CODMUNICIPIO0.2630.6900.5640.5640.7860.7850.0280.0120.0820.9150.8090.1580.0670.0710.1210.1041.0001.0000.002-0.0350.0090.0160.1420.0280.0300.1080.0900.1270.1680.1790.184-0.2200.263-0.0520.031-0.0330.071-0.084-0.052-0.0380.008
ESTU_PRGM_DEPARTAMENTO0.2110.5810.5400.5400.0130.7000.0610.0330.1020.8880.8920.2460.1660.1370.1380.1641.0001.0000.0030.1830.0130.0410.2180.0480.0510.1710.1450.2280.2370.2090.2680.2440.3470.0580.0550.0350.1000.0840.0560.0700.023
ESTU_PRIVADO_LIBERTAD0.0100.0010.0030.0030.0000.0000.0000.0070.0020.0040.0050.0120.0000.0010.0000.0000.0020.0031.0000.0040.0000.0020.0010.0000.0000.0000.0000.0010.0000.0010.0000.0030.0050.0000.0010.0100.0000.0020.0000.0000.003
ESTU_SNIES_PRGMACADEMICO-0.1110.0090.0180.0190.0340.0360.0050.0810.081-0.0310.1910.3900.0280.0300.1910.203-0.0350.1830.0041.0000.0110.0300.2130.1010.0940.0780.1110.0410.0310.0320.3030.4700.153-0.2050.061-0.1250.103-0.229-0.228-0.2380.079
ESTU_TIPODOCUMENTO0.0060.0060.0080.0080.0970.0170.0000.0090.0100.0080.0140.0130.0070.0180.0120.0120.0090.0130.0000.0111.0000.3640.0170.0140.0130.0360.0170.0090.0110.0080.0240.0150.0140.0050.0080.0060.0270.0210.0060.0080.018
ESTU_TIPODOCUMENTOSB110.0240.0240.0210.0210.0210.0270.0030.0620.0350.0140.0410.0810.0380.0200.0850.1140.0160.0410.0020.0300.3641.0000.0370.0660.0530.0370.0190.0370.0410.0400.0420.0400.0200.0730.0560.0460.0800.0790.0800.0860.014
ESTU_VALORMATRICULAUNIVERSIDAD0.1250.0950.0910.0910.0020.1060.0110.0630.1380.1620.2350.3800.1990.2720.2730.3400.1420.2180.0010.2130.0170.0371.0000.1520.1470.1980.3140.1520.1820.1700.3050.2350.3520.1080.1080.0730.2280.1710.1150.1090.088
FAMI_EDUCACIONMADRE0.1030.0370.0430.0430.0060.0440.0070.0660.0980.0250.0480.1980.0410.0350.3200.2720.0280.0480.0000.1010.0140.0660.1521.0000.2940.1740.3060.1450.1810.1720.1270.0960.0580.0850.0720.0430.2010.1550.0880.0880.031
FAMI_EDUCACIONPADRE0.1010.0430.0430.0430.0060.0480.0050.0680.0920.0280.0490.1780.0470.0370.2870.2420.0300.0510.0000.0940.0130.0530.1470.2941.0000.1800.3030.1400.1760.1690.1180.0890.0570.0820.0730.0440.1990.1530.0840.0840.033
FAMI_ESTRATOVIVIENDA0.1480.1130.0930.0930.0420.1210.0060.0570.0750.1070.1640.1250.1220.0550.2120.1460.1080.1710.0000.0780.0360.0370.1980.1740.1801.0000.4310.2650.3350.2810.0980.0730.1190.0910.0770.0580.2110.1810.0890.0850.032
FAMI_TIENEAUTOMOVIL0.2660.1040.0900.0900.0090.1020.0040.0630.0810.0950.1480.1150.0920.0470.1870.1030.0900.1450.0000.1110.0170.0190.3140.3060.3030.4311.0000.1760.1930.2270.1080.1060.1780.1210.0730.0770.2360.2430.1150.1360.030
FAMI_TIENECOMPUTADOR0.0950.1100.1880.1880.0030.1300.0050.0140.0380.1350.2170.0510.0500.0150.0600.0200.1270.2280.0010.0410.0090.0370.1520.1450.1400.2650.1761.0000.4380.2030.0330.0400.1180.1240.0440.0610.1470.1690.1120.1170.064
FAMI_TIENEINTERNET0.1140.1790.2250.2250.0050.1670.0090.0140.0550.1650.2240.0820.0500.0050.0550.0230.1680.2370.0000.0310.0110.0410.1820.1810.1760.3350.1930.4381.0000.2710.0230.0350.1460.1300.0430.0530.1850.1940.1210.1110.075
FAMI_TIENELAVADORA0.1080.1980.1270.1270.0050.1690.0060.0080.0550.1700.1990.0680.0590.0060.0520.0220.1790.2090.0010.0320.0080.0400.1700.1720.1690.2810.2270.2030.2711.0000.0190.0390.1290.0930.0440.0510.1370.1490.0890.0900.028
INST_CARACTER_ACADEMICO0.0790.1090.0840.0840.0010.1140.0040.0740.1260.2100.2840.2490.0800.0870.2090.2270.1840.2680.0000.3030.0240.0420.3050.1270.1180.0980.1080.0330.0230.0191.0000.7020.2210.1320.0770.0790.1580.1610.1480.1410.037
INST_COD_INSTITUCION-0.087-0.072-0.072-0.072-0.076-0.0750.0080.0850.120-0.2380.2690.3290.1060.1120.2360.248-0.2200.2440.0030.4700.0150.0400.2350.0960.0890.0730.1060.0400.0350.0390.7021.0000.236-0.2270.082-0.1270.141-0.239-0.267-0.2480.010
INST_ORIGEN0.0940.1830.1350.1350.0110.1750.0060.0680.0630.2850.3770.2290.1670.2800.0490.0620.2630.3470.0050.1530.0140.0200.3520.0580.0570.1190.1780.1180.1460.1290.2210.2361.0000.0460.0320.0260.0700.0630.0490.0550.021
MOD_COMPETEN_CIUDADA_PUNT0.080-0.080-0.113-0.114-0.100-0.1020.0100.1050.067-0.0390.0550.1390.0790.0490.0950.144-0.0520.0580.000-0.2050.0050.0730.1080.0850.0820.0910.1210.1240.1300.0930.132-0.2270.0461.0000.1660.2830.2690.5350.7000.5310.025
MOD_COMUNI_ESCRITA_DESEM0.0510.0400.0400.0400.0010.0490.0070.0330.0470.0230.0520.0800.0420.0200.0650.0990.0310.0550.0010.0610.0080.0560.1080.0720.0730.0770.0730.0440.0430.0440.0770.0820.0320.1661.0000.9110.1480.1570.1790.1510.085
MOD_COMUNI_ESCRITA_PUNT0.046-0.0520.0120.012-0.065-0.0670.0090.0360.043-0.0240.0330.0820.0450.0210.0690.101-0.0330.0350.010-0.1250.0060.0460.0730.0430.0440.0580.0770.0610.0530.0510.079-0.1270.0260.2830.9111.0000.1310.2600.3150.275-0.153
MOD_INGLES_DESEM0.1420.0880.1290.1290.0180.0940.0120.1000.0900.0540.0950.1990.0620.0660.2060.2270.0710.1000.0000.1030.0270.0800.2280.2010.1990.2110.2360.1470.1850.1370.1580.1410.0700.2690.1480.1311.0000.6630.2750.2410.198
MOD_INGLES_PUNT0.183-0.127-0.169-0.170-0.151-0.1550.0100.1020.094-0.0600.0800.2070.0420.0640.2170.230-0.0840.0840.002-0.2290.0210.0790.1710.1550.1530.1810.2430.1690.1940.1490.161-0.2390.0630.5350.1570.2600.6631.0000.5480.5060.061
MOD_LECTURA_CRITICA_PUNT0.080-0.089-0.093-0.094-0.109-0.1120.0110.0600.077-0.0340.0540.1610.0880.0540.1240.179-0.0520.0560.000-0.2280.0060.0800.1150.0880.0840.0890.1150.1120.1210.0890.148-0.2670.0490.7000.1790.3150.2750.5481.0000.597-0.005
MOD_RAZONA_CUANTITAT_PUNT0.081-0.068-0.073-0.074-0.087-0.0890.0070.2290.064-0.0180.0690.1590.0830.0600.1080.138-0.0380.0700.000-0.2380.0080.0860.1090.0880.0840.0850.1360.1170.1110.0900.141-0.2480.0550.5310.1510.2750.2410.5060.5971.000-0.048
PERIODO0.0090.000-0.187-0.1870.0150.0160.0440.0100.0330.0080.0240.0500.1020.0310.0440.0130.0080.0230.0030.0790.0180.0140.0880.0310.0330.0320.0300.0640.0750.0280.0370.0100.0210.0250.085-0.1530.1980.061-0.005-0.0481.000

Missing values

2025-05-21T17:25:46.525614image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-05-21T17:25:58.126444image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2025-05-21T17:28:29.582005image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

PERIODOESTU_CONSECUTIVOESTU_TIPODOCUMENTOESTU_PAIS_RESIDEESTU_COD_RESIDE_DEPTOESTU_DEPTO_RESIDEESTU_COD_RESIDE_MCPIOESTU_MCPIO_RESIDEESTU_CODDANE_COLE_TERMINOESTU_COD_COLE_MCPIO_TERMINOESTU_COD_DEPTO_PRESENTACIONINST_COD_INSTITUCIONINST_NOMBRE_INSTITUCIONINST_CARACTER_ACADEMICOESTU_NUCLEO_PREGRADOESTU_INST_DEPARTAMENTOESTU_INST_CODMUNICIPIOESTU_INST_MUNICIPIOESTU_PRGM_ACADEMICOESTU_PRGM_DEPARTAMENTOESTU_PRGM_CODMUNICIPIOESTU_PRGM_MUNICIPIOESTU_NIVEL_PRGM_ACADEMICOESTU_METODO_PRGMESTU_VALORMATRICULAUNIVERSIDADESTU_DEPTO_PRESENTACIONESTU_COD_MCPIO_PRESENTACIONESTU_MCPIO_PRESENTACIONESTU_PAGOMATRICULABECAESTU_PAGOMATRICULACREDITOESTU_HORASSEMANATRABAJAESTU_SNIES_PRGMACADEMICOESTU_PRIVADO_LIBERTADESTU_NACIONALIDADESTU_ESTUDIANTEESTU_GENEROESTU_COLE_TERMINOESTU_PAGOMATRICULAPADRESESTU_ESTADOINVESTIGACIONESTU_FECHANACIMIENTOESTU_PAGOMATRICULAPROPIOESTU_TIPODOCUMENTOSB11FAMI_EDUCACIONPADREFAMI_TIENEAUTOMOVILFAMI_TIENELAVADORAFAMI_ESTRATOVIVIENDAFAMI_TIENECOMPUTADORFAMI_TIENEINTERNETFAMI_EDUCACIONMADREINST_ORIGENMOD_RAZONA_CUANTITAT_PUNTMOD_COMUNI_ESCRITA_PUNTMOD_COMUNI_ESCRITA_DESEMMOD_INGLES_DESEMMOD_LECTURA_CRITICA_PUNTMOD_INGLES_PUNTMOD_COMPETEN_CIUDADA_PUNT
020183EK201830011083CCCOLOMBIA11.0BOGOTÁ11001.0BOGOTÁ D.C.NaNNaN11.02834UNIVERSITARIA AGUSTINIANA- UNIAGUSTINIANA-BOGOTÁ D.C.INSTITUCIÓN UNIVERSITARIAADMINISTRACIÓNBOGOTÁ11001BOGOTÁ D.C.HOTELERIA Y TURISMOBOGOTÁ11001BOGOTÁ D.C.UNIVERSITARIOPRESENCIALEntre 2.5 millones y menos de 4 millonesBOGOTÁ11001.0BOGOTÁ D.C.NoNoEntre 11 y 20 horas54487.0NCOLOMBIAESTUDIANTEFNaNSiPUBLICAR18/09/1997NoCCPrimaria completaSiSiEstrato 2SiSiSecundaria (Bachillerato) incompletaNO OFICIAL - CORPORACIÓN161174.03.0A2139161.0128
120183EK201830053875CCCOLOMBIA76.0VALLE76736.0SEVILLANaNNaN76.01203UNIVERSIDAD DEL VALLE-CALIUNIVERSIDADCONTADURÍA PUBLICAVALLE76001CALICONTADURIA PUBLICAVALLE76834TULUÁUNIVERSITARIOPRESENCIALMenos de 500 milVALLE76834.0TULUÁNoNoEntre 21 y 30 horas52346.0NCOLOMBIAESTUDIANTEMNaNSiPUBLICAR24/07/1995NoTIPrimaria incompletaNoSiEstrato 2SiSiSecundaria (Bachillerato) incompletaOFICIAL DEPARTAMENTAL147170.03.0B1171188.0182
220183EK201830167993CCCOLOMBIA73.0TOLIMA73001.0IBAGUÉ1.730010e+1173001.073.02829CORPORACION UNIVERSITARIA MINUTO DE DIOS -UNIMINUTO-BOGOTÁ D.C.INSTITUCIÓN UNIVERSITARIACONTADURÍA PUBLICABOGOTÁ11001BOGOTÁ D.C.CONTADURÍA PÚBLICABOGOTÁ11001BOGOTÁ D.C.UNIVERSITARIODISTANCIAEntre 1 millón y menos de 2.5 millonesTOLIMA73001.0IBAGUÉNoNoMás de 30 horas91334.0NCOLOMBIAESTUDIANTEMSEDE 1 GERMAN PARDONoPUBLICAR18/03/1994SiCCNo AplicaNoSiEstrato 2SiSiNo sabeNO OFICIAL - CORPORACIÓN14277.01.0A1124130.0153
320183EK201830168158CCCOLOMBIA68.0SANTANDER68081.0BARRANCABERMEJA1.680810e+1168081.068.02207INSTITUTO UNIVERSITARIO DE LA PAZ-BARRANCABERMEJAINSTITUCIÓN UNIVERSITARIAINGENIERÍA AMBIENTAL, SANITARIA Y AFINESSANTANDER68081BARRANCABERMEJAINGENIERIA AMBIENTAL Y DE SANEAMIENTOSANTANDER68081BARRANCABERMEJAUNIVERSITARIOPRESENCIALEntre 500 mil y menos de 1 millónSANTANDER68081.0BARRANCABERMEJANoNo03127.0NCOLOMBIAESTUDIANTEMINSTITUTO TECNICO INDUSTRIALSiPUBLICAR29/06/1997NoTIPostgradoNoSiEstrato 2SiSiTécnica o tecnológica completaOFICIAL DEPARTAMENTAL185157.03.0-A1178106.0169
420183EK201830164354CCCOLOMBIA11.0BOGOTÁ11001.0BOGOTÁ D.C.1.110010e+1111001.011.01208UNIVERSIDAD DEL QUINDIO-ARMENIAUNIVERSIDADBIBLIOTECOLOGÍA, OTROS DE CIENCIAS SOCIALES Y HUMANASQUINDIO63001ARMENIACIENCIAS DE LA INFORMACION Y LA DOCUMENTACIONQUINDIO63001ARMENIAUNIVERSITARIODISTANCIA VITUALEntre 500 mil y menos de 1 millónBOGOTÁ11001.0BOGOTÁ D.C.NoNoEntre 21 y 30 horas833.0NCOLOMBIAESTUDIANTEFCENT EDUC DIST NUEVA CONSTITUCIONNoPUBLICAR23/09/1987SiTIEducación profesional completaNoSiEstrato 3SiSiEducación profesional completaOFICIAL DEPARTAMENTAL114132.02.0A1139135.0126
520183EK201830129217CCCOLOMBIA70.0SUCRE70001.0SINCELEJO1.700010e+1170001.070.02823CORPORACION UNIVERSITARIA DEL CARIBE - CECAR-SINCELEJOINSTITUCIÓN UNIVERSITARIAPSICOLOGÍASUCRE70001SINCELEJOPSICOLOGIASUCRE70001SINCELEJOUNIVERSITARIOPRESENCIALEntre 1 millón y menos de 2.5 millonesSUCRE70001.0SINCELEJONoSi05223.0NCOLOMBIAESTUDIANTEFINSTITUCION EDUCATIVA NORMAL SUPERIOR DE SINCELEJONoPUBLICAR28/06/1996NoCCEducación profesional incompletaNoSiEstrato 1NoNoNingunoNO OFICIAL - CORPORACIÓN120127.02.0-A1152121.0127
620183EK201830138320CCCOLOMBIA11.0BOGOTÁ11001.0BOGOTÁ D.C.3.110010e+1111001.011.01701PONTIFICIA UNIVERSIDAD JAVERIANA-BOGOTÁ D.C.UNIVERSIDADADMINISTRACIÓNBOGOTÁ11001BOGOTÁ D.C.ADMINISTRACION DE EMPRESASBOGOTÁ11001BOGOTÁ D.C.UNIVERSITARIOPRESENCIALMás de 7 millonesBOGOTÁ11001.0BOGOTÁ D.C.NoSi0953.0NCOLOMBIAESTUDIANTEFCOLEGIO AMERICANO DE BOGOTASiPUBLICAR27/01/1997SiTIEducación profesional incompletaSiSiEstrato 4SiSiTécnica o tecnológica incompletaNO OFICIAL - FUNDACIÓN128165.03.0A2169159.0124
720183EK201830176603CCCOLOMBIA76.0VALLE76520.0PALMIRANaNNaN76.02102UNIVERSIDAD NACIONAL ABIERTA Y A DISTANCIA UNAD-BOGOTÁ D.C.UNIVERSIDADPSICOLOGÍABOGOTÁ11001BOGOTÁ D.C.PSICOLOGIABOGOTÁ11001BOGOTÁ D.C.UNIVERSITARIODISTANCIAEntre 1 millón y menos de 2.5 millonesVALLE76520.0PALMIRANoNoMenos de 10 horas3274.0NCOLOMBIAESTUDIANTEMNaNNoPUBLICAR04/04/1981SiCCTécnica o tecnológica completaSiSiEstrato 3SiSiTécnica o tecnológica completaOFICIAL NACIONAL108157.03.0-A1148115.0142
820183EK201830071262CCCOLOMBIA11.0BOGOTÁ11001.0BOGOTÁ D.C.NaNNaN11.02725POLITECNICO GRANCOLOMBIANO-BOGOTÁ D.C.INSTITUCIÓN UNIVERSITARIAADMINISTRACIÓNBOGOTÁ11001BOGOTÁ D.C.NEGOCIOS INTERNACIONALESBOGOTÁ11001BOGOTÁ D.C.UNIVERSITARIODISTANCIA VITUALEntre 1 millón y menos de 2.5 millonesBOGOTÁ11001.0BOGOTÁ D.C.NoNoNaN101492.0NCOLOMBIAESTUDIANTEMNaNNoPUBLICAR12/12/1981SiCCNaNNaNNaNNaNNaNNaNNaNNO OFICIAL - FUNDACIÓN132116.02.0A1101123.0107
920183EK201830045596CCCOLOMBIA76.0VALLE76001.0CALI3.760010e+1176001.076.02745UNIPANAMERICANA - FUNDACION UNIVERSITARIA PANAMERICANA-BOGOTÁ D.C.INSTITUCIÓN UNIVERSITARIAADMINISTRACIÓNBOGOTÁ11001BOGOTÁ D.C.ADMINISTRACIÓN DE EMPRESASVALLE76001CALIUNIVERSITARIOPRESENCIALEntre 1 millón y menos de 2.5 millonesVALLE76001.0CALISiSiMás de 30 horas91342.0NCOLOMBIAESTUDIANTEFCOLEGIO NUESTRA SEÑORA DE LA CONSOLACIONNoPUBLICAR14/11/1968SiTIPrimaria completaSiSiEstrato 3SiSiPrimaria completaNO OFICIAL - FUNDACIÓN127136.02.0A1119139.0121
PERIODOESTU_CONSECUTIVOESTU_TIPODOCUMENTOESTU_PAIS_RESIDEESTU_COD_RESIDE_DEPTOESTU_DEPTO_RESIDEESTU_COD_RESIDE_MCPIOESTU_MCPIO_RESIDEESTU_CODDANE_COLE_TERMINOESTU_COD_COLE_MCPIO_TERMINOESTU_COD_DEPTO_PRESENTACIONINST_COD_INSTITUCIONINST_NOMBRE_INSTITUCIONINST_CARACTER_ACADEMICOESTU_NUCLEO_PREGRADOESTU_INST_DEPARTAMENTOESTU_INST_CODMUNICIPIOESTU_INST_MUNICIPIOESTU_PRGM_ACADEMICOESTU_PRGM_DEPARTAMENTOESTU_PRGM_CODMUNICIPIOESTU_PRGM_MUNICIPIOESTU_NIVEL_PRGM_ACADEMICOESTU_METODO_PRGMESTU_VALORMATRICULAUNIVERSIDADESTU_DEPTO_PRESENTACIONESTU_COD_MCPIO_PRESENTACIONESTU_MCPIO_PRESENTACIONESTU_PAGOMATRICULABECAESTU_PAGOMATRICULACREDITOESTU_HORASSEMANATRABAJAESTU_SNIES_PRGMACADEMICOESTU_PRIVADO_LIBERTADESTU_NACIONALIDADESTU_ESTUDIANTEESTU_GENEROESTU_COLE_TERMINOESTU_PAGOMATRICULAPADRESESTU_ESTADOINVESTIGACIONESTU_FECHANACIMIENTOESTU_PAGOMATRICULAPROPIOESTU_TIPODOCUMENTOSB11FAMI_EDUCACIONPADREFAMI_TIENEAUTOMOVILFAMI_TIENELAVADORAFAMI_ESTRATOVIVIENDAFAMI_TIENECOMPUTADORFAMI_TIENEINTERNETFAMI_EDUCACIONMADREINST_ORIGENMOD_RAZONA_CUANTITAT_PUNTMOD_COMUNI_ESCRITA_PUNTMOD_COMUNI_ESCRITA_DESEMMOD_INGLES_DESEMMOD_LECTURA_CRITICA_PUNTMOD_INGLES_PUNTMOD_COMPETEN_CIUDADA_PUNT
121747220222EK202220135693CCCOLOMBIA18.0CAQUETA18256.0EL PAUJÍLNaNNaN11.02829CORPORACION UNIVERSITARIA MINUTO DE DIOS -UNIMINUTO-BOGOTÁ D.C.INSTITUCIÓN UNIVERSITARIAPSICOLOGÍABOGOTÁ11001BOGOTÁ D.C.PSICOLOGÍAHUILA41001NEIVAUNIVERSITARIODISTANCIAEntre 1 millón y menos de 2.5 millonesBOGOTÁ11001.0BOGOTÁ D.C.NoNoEntre 21 y 30 horas91141.0NCOLOMBIAESTUDIANTEFNaNNoPUBLICAR06/11/1996SiCCPrimaria incompletaNoSiEstrato 1SiSiPrimaria incompletaNO OFICIAL - CORPORACIÓN700.0NaNA1104114.098
121747320222EK202220135960CCCOLOMBIA5.0ANTIOQUIA5686.0SANTA ROSA DE OSOS1.056860e+115686.011.02732FUNDACION UNIVERSITARIA CATOLICA DEL NORTE-SANTA ROSA DE OSOSINSTITUCIÓN UNIVERSITARIASOCIOLOGÍA, TRABAJO SOCIAL Y AFINESANTIOQUIA5686SANTA ROSA DE OSOSTRABAJO SOCIALANTIOQUIA5686SANTA ROSA DE OSOSUNIVERSITARIODISTANCIA VITUALEntre 500 mil y menos de 1 millónBOGOTÁ11001.0BOGOTÁ D.C.SiNoMás de 30 horas106836.0NCOLOMBIAESTUDIANTEFI. E CARDENAL ANIBAL MUÑOZ DUQUENoPUBLICAR05/08/1983SiTIPrimaria incompletaNoSiEstrato 3SiSiPrimaria incompletaNO OFICIAL - FUNDACIÓN98151.03.0A1119120.0136
121747420222EK202220194751CCCOLOMBIA5.0ANTIOQUIA5045.0APARTADÓ1.050450e+115045.011.02725POLITECNICO GRANCOLOMBIANO-BOGOTÁ D.C.INSTITUCIÓN UNIVERSITARIAADMINISTRACIÓNBOGOTÁ11001BOGOTÁ D.C.ADMINISTRACIÓN DE EMPRESASBOGOTÁ11001BOGOTÁ D.C.UNIVERSITARIODISTANCIA VITUALEntre 1 millón y menos de 2.5 millonesBOGOTÁ11001.0BOGOTÁ D.C.NoNoMenos de 10 horas90399.0NCOLOMBIAESTUDIANTEFINSTITUCIÓN EDUCATIVA JOSÉ CELESTINO MUTISNoPUBLICAR24/11/1995SiCCPrimaria incompletaSiSiEstrato 2SiSiPrimaria incompletaNO OFICIAL - FUNDACIÓN12295.01.0A1104119.0104
121747520222EK202220206584CCCOLOMBIA11.0BOGOTÁ11001.0BOGOTÁ D.C.2.111020e+1111001.011.04721FUNDACION UNIVERSITARIA HORIZONTE-BOGOTÁ D.C.INSTITUCIÓN UNIVERSITARIAINGENIERÍA ADMINISTRATIVA Y AFINESBOGOTÁ11001BOGOTÁ D.C.INGENIERIA EN SEGURIDAD INDUSTRIAL E HIGIENE OCUPACIONALBOGOTÁ11001BOGOTÁ D.C.UNIVERSITARIOPRESENCIALEntre 2.5 millones y menos de 4 millonesBOGOTÁ11001.0BOGOTÁ D.C.NoNoMenos de 10 horas104564.0NCOLOMBIAESTUDIANTEFCENT EDUC DIST EL PORVENIRNoPUBLICAR23/01/1991SiTISecundaria (Bachillerato) incompletaNoSiEstrato 3SiSiSecundaria (Bachillerato) completaNO OFICIAL - FUNDACIÓN168125.02.0A2144156.0134
121747620223EK202230220834CCCOLOMBIANaNNaNNaNNaNNaNNaN11.01101UNIVERSIDAD NACIONAL DE COLOMBIA-BOGOTÁ D.C.UNIVERSIDADINGENIERÍA MECÁNICA Y AFINESBOGOTÁ11001BOGOTÁ D.C.INGENIERIA MECATRONICABOGOTÁ11001BOGOTÁ D.C.UNIVERSITARIOPRESENCIALNaNBOGOTÁ11001.0BOGOTÁ D.C.NaNNaNNaN16939.0NCOLOMBIAESTUDIANTEFNaNNaNPUBLICAR17/02/1999NaNNaNNaNNaNNaNNaNNaNNaNNaNOFICIAL NACIONAL221156.03.0B2168198.0104
121747720222EK202220148630CCCOLOMBIA41.0HUILA41396.0LA PLATA2.413960e+1141396.011.01110UNIVERSIDAD DEL CAUCA-POPAYANUNIVERSIDADDERECHO Y AFINESCAUCA19001POPAYÁNDERECHOCAUCA19001POPAYÁNUNIVERSITARIOPRESENCIALNo pagó matrículaBOGOTÁ11001.0BOGOTÁ D.C.NoNoEntre 11 y 20 horas233.0NCOLOMBIAESTUDIANTEMSAN SEBASTIANNoPUBLICAR02/04/1997NoTIEducación profesional completaSiSiEstrato 2SiSiPrimaria incompletaOFICIAL NACIONAL101149.02.0A2153134.0140
121747820222EK202220164335CCCOLOMBIA54.0NORTE SANTANDER54810.0TIBÚ1.548100e+1154810.054.02728FUNDACION UNIVERSITARIA DEL AREA ANDINA-BOGOTÁ D.C.INSTITUCIÓN UNIVERSITARIAEDUCACIÓNBOGOTÁ11001BOGOTÁ D.C.LICENCIATURA EN PEDAGOGÍA INFANTILBOGOTÁ11001BOGOTÁ D.C.UNIVERSITARIODISTANCIA VITUALEntre 1 millón y menos de 2.5 millonesNORTE SANTANDER54001.0CÚCUTANoNoEntre 21 y 30 horas102554.0NCOLOMBIAESTUDIANTEFCOL INTEG FRANCISCO JOSE DE CALDASNoPUBLICAR10/05/1994SiCCPrimaria incompletaNoSiEstrato 1SiNoPrimaria completaNO OFICIAL - FUNDACIÓN91135.02.0A2100132.093
121747920222EK202220142587CCCOLOMBIA11.0BOGOTÁ11001.0BOGOTÁ D.C.3.230010e+1123001.011.01709UNIVERSIDAD CENTRAL-BOGOTÁ D.C.UNIVERSIDADPUBLICIDAD Y AFINESBOGOTÁ11001BOGOTÁ D.C.PUBLICIDADBOGOTÁ11001BOGOTÁ D.C.UNIVERSITARIOPRESENCIALEntre 4 millones y menos de 5.5 millonesBOGOTÁ11001.0BOGOTÁ D.C.NoNoEntre 21 y 30 horas1169.0NCOLOMBIAESTUDIANTEFLICEO MONTERIASiPUBLICAR26/10/1994SiTISecundaria (Bachillerato) incompletaNoSiEstrato 3NoSiSecundaria (Bachillerato) completaNO OFICIAL - FUNDACIÓN159162.03.0B1190178.0163
121748020222EK202220116355CCCOLOMBIA5.0ANTIOQUIA5001.0MEDELLÍN1.050010e+115001.011.02110COLEGIO MAYOR DE ANTIOQUIA-MEDELLININSTITUCIÓN UNIVERSITARIAINGENIERÍA AMBIENTAL, SANITARIA Y AFINESANTIOQUIA5001MEDELLÍNINGENIERIA AMBIENTALANTIOQUIA5001MEDELLÍNUNIVERSITARIOPRESENCIALEntre 1 millón y menos de 2.5 millonesBOGOTÁ11001.0BOGOTÁ D.C.SiNoMás de 30 horas54263.0NCOLOMBIAESTUDIANTEFINSTITUCIÓN EDUCATIVA CONCEJO DE MEDELLINNoPUBLICAR18/07/1998NoCCSecundaria (Bachillerato) incompletaNoSiEstrato 3SiSiSecundaria (Bachillerato) incompletaOFICIAL NACIONAL179156.03.0A2137134.0154
121748120222EK202220153024CCCOLOMBIA76.0VALLE76001.0CALINaNNaN11.01807UNIVERSIDAD LIBRE-CALIUNIVERSIDADADMINISTRACIÓNVALLE76001CALIMERCADEOVALLE76001CALIUNIVERSITARIOPRESENCIALEntre 2.5 millones y menos de 4 millonesBOGOTÁ11001.0BOGOTÁ D.C.NoNoNaN52206.0NCOLOMBIAESTUDIANTEFNaNNoPUBLICAR05/03/1983SiTINaNNaNNaNNaNNaNNaNNaNNO OFICIAL - CORPORACIÓN142129.02.0A1155120.0114

Duplicate rows

Most frequently occurring

PERIODOESTU_CONSECUTIVOESTU_TIPODOCUMENTOESTU_PAIS_RESIDEESTU_COD_RESIDE_DEPTOESTU_DEPTO_RESIDEESTU_COD_RESIDE_MCPIOESTU_MCPIO_RESIDEESTU_CODDANE_COLE_TERMINOESTU_COD_COLE_MCPIO_TERMINOESTU_COD_DEPTO_PRESENTACIONINST_COD_INSTITUCIONINST_NOMBRE_INSTITUCIONINST_CARACTER_ACADEMICOESTU_NUCLEO_PREGRADOESTU_INST_DEPARTAMENTOESTU_INST_CODMUNICIPIOESTU_INST_MUNICIPIOESTU_PRGM_ACADEMICOESTU_PRGM_DEPARTAMENTOESTU_PRGM_CODMUNICIPIOESTU_PRGM_MUNICIPIOESTU_NIVEL_PRGM_ACADEMICOESTU_METODO_PRGMESTU_VALORMATRICULAUNIVERSIDADESTU_DEPTO_PRESENTACIONESTU_COD_MCPIO_PRESENTACIONESTU_MCPIO_PRESENTACIONESTU_PAGOMATRICULABECAESTU_PAGOMATRICULACREDITOESTU_HORASSEMANATRABAJAESTU_SNIES_PRGMACADEMICOESTU_PRIVADO_LIBERTADESTU_NACIONALIDADESTU_ESTUDIANTEESTU_GENEROESTU_COLE_TERMINOESTU_PAGOMATRICULAPADRESESTU_ESTADOINVESTIGACIONESTU_FECHANACIMIENTOESTU_PAGOMATRICULAPROPIOESTU_TIPODOCUMENTOSB11FAMI_EDUCACIONPADREFAMI_TIENEAUTOMOVILFAMI_TIENELAVADORAFAMI_ESTRATOVIVIENDAFAMI_TIENECOMPUTADORFAMI_TIENEINTERNETFAMI_EDUCACIONMADREINST_ORIGENMOD_RAZONA_CUANTITAT_PUNTMOD_COMUNI_ESCRITA_PUNTMOD_COMUNI_ESCRITA_DESEMMOD_INGLES_DESEMMOD_LECTURA_CRITICA_PUNTMOD_INGLES_PUNTMOD_COMPETEN_CIUDADA_PUNT# duplicates
020225EK202250069558CCCOLOMBIA52.0NARIÑO52001.0PASTO3.520010e+1152001.052.01206UNIVERSIDAD DE NARIÑO-PASTOUNIVERSIDADARQUITECTURANARIÑO52001PASTOARQUITECTURANARIÑO52001PASTOUNIVERSITARIOPRESENCIALNo pagó matrículaNARIÑO52001.0PASTOSiNoEntre 21 y 30 horas19127.0NCOLOMBIAESTUDIANTEMINSTITUTO EDUCATIVO ALBERTO QUIJANO VODNIZA - SEDE PRINCIPALNoPUBLICAR18/10/1996NoTITécnica o tecnológica incompletaNoNoEstrato 1SiSiTécnica o tecnológica completaOFICIAL DEPARTAMENTAL134151.03.0A2126129.01372
120225EK202250069574CCCOLOMBIA52.0NARIÑO52001.0PASTO1.520010e+1152001.052.01206UNIVERSIDAD DE NARIÑO-PASTOUNIVERSIDADARQUITECTURANARIÑO52001PASTOARQUITECTURANARIÑO52001PASTOUNIVERSITARIOPRESENCIALNo pagó matrículaNARIÑO52001.0PASTONoNoEntre 11 y 20 horas19127.0NCOLOMBIAESTUDIANTEMI.E.M. CIUDAD DE PASTO - SEDE PRINCIPALSiPUBLICAR12/09/1995SiTIPrimaria incompletaNoSiEstrato 3SiSiSecundaria (Bachillerato) completaOFICIAL DEPARTAMENTAL191166.03.0B1154180.01942
220225EK202250069895CCCOLOMBIA52.0NARIÑO52001.0PASTO1.520010e+1152001.052.01206UNIVERSIDAD DE NARIÑO-PASTOUNIVERSIDADARQUITECTURANARIÑO52001PASTOARQUITECTURANARIÑO52001PASTOUNIVERSITARIOPRESENCIALNo pagó matrículaNARIÑO52001.0PASTONoNoEntre 11 y 20 horas19127.0NCOLOMBIAESTUDIANTEMI.E.M. NORMAL SUPERIOR DE PASTO - SEDE PRINCIPALNoPUBLICAR20/09/1994SiTISecundaria (Bachillerato) completaNoSiEstrato 2SiSiSecundaria (Bachillerato) completaOFICIAL DEPARTAMENTAL177166.03.0A2157128.01582
320225EK202250069923CCCOLOMBIA86.0PUTUMAYO86760.0SANTIAGO3.867490e+1186749.052.01206UNIVERSIDAD DE NARIÑO-PASTOUNIVERSIDADARQUITECTURANARIÑO52001PASTOARQUITECTURANARIÑO52001PASTOUNIVERSITARIOPRESENCIALMenos de 500 milNARIÑO52001.0PASTOSiNo019127.0NCOLOMBIAESTUDIANTEFCOL CHAMPAGNATSiPUBLICAR09/09/2000SiTITécnica o tecnológica completaNoNoEstrato 1SiSiTécnica o tecnológica completaOFICIAL DEPARTAMENTAL130159.03.0A2144131.01382
420225EK202250069971CCCOLOMBIA52.0NARIÑO52356.0IPIALES1.523560e+1152356.052.01206UNIVERSIDAD DE NARIÑO-PASTOUNIVERSIDADARQUITECTURANARIÑO52001PASTOARQUITECTURANARIÑO52001PASTOUNIVERSITARIOPRESENCIALNo pagó matrículaNARIÑO52001.0PASTOSiNoEntre 11 y 20 horas19127.0NCOLOMBIAESTUDIANTEMINSTITUCION EDUCATIVA NACIONAL SUCRENoPUBLICAR30/03/1996NoTISecundaria (Bachillerato) incompletaNoNoEstrato 1SiNoSecundaria (Bachillerato) incompletaOFICIAL DEPARTAMENTAL178166.03.0A2170142.01382
520225EK202250069989CCCOLOMBIA52.0NARIÑO52001.0PASTO1.520010e+1152001.052.01206UNIVERSIDAD DE NARIÑO-PASTOUNIVERSIDADARQUITECTURANARIÑO52001PASTOARQUITECTURANARIÑO52001PASTOUNIVERSITARIOPRESENCIALNo pagó matrículaNARIÑO52001.0PASTOSiNoMás de 30 horas19127.0NCOLOMBIAESTUDIANTEFI.E.M. LIC CENTRAL DE NARIÑO - SEDE PRINCIPALNoPUBLICAR05/09/1999SiTISecundaria (Bachillerato) completaNoNoEstrato 1SiSiSecundaria (Bachillerato) completaOFICIAL DEPARTAMENTAL168144.02.0A2161133.0842
620225EK202250070545CCCOLOMBIA8.0ATLANTICO8001.0BARRANQUILLA3.200010e+1120001.08.01804UNIVERSIDAD AUTONOMA DEL CARIBE-BARRANQUILLAUNIVERSIDADARQUITECTURAATLANTICO8001BARRANQUILLAARQUITECTURAATLANTICO8001BARRANQUILLAUNIVERSITARIOPRESENCIALEntre 4 millones y menos de 5.5 millonesATLANTICO8001.0BARRANQUILLANoSi01466.0NCOLOMBIAESTUDIANTEMGIMSABER - SEDE PRINCIPALSiPUBLICAR14/06/2001NoTIEducación profesional completaNoNoEstrato 2SiSiEducación profesional completaNO OFICIAL - CORPORACIÓN130159.03.0B1142179.01182
720225EK202250070562CCCOLOMBIA88.0SAN ANDRES88001.0SAN ANDRÉS3.880010e+1188001.08.01804UNIVERSIDAD AUTONOMA DEL CARIBE-BARRANQUILLAUNIVERSIDADARQUITECTURAATLANTICO8001BARRANQUILLAARQUITECTURAATLANTICO8001BARRANQUILLAUNIVERSITARIOPRESENCIALEntre 2.5 millones y menos de 4 millonesATLANTICO8001.0BARRANQUILLASiSiEntre 21 y 30 horas1466.0NCOLOMBIAESTUDIANTEMI.E. DE LA SAGRADA FAMILIA - SEDE PRINCIPALSiPUBLICAR16/05/1995SiTISecundaria (Bachillerato) completaSiSiEstrato 3NoNoEducación profesional completaNO OFICIAL - CORPORACIÓN143154.03.0A2167162.01512
820225EK202250071273CCCOLOMBIA8.0ATLANTICO8001.0BARRANQUILLA3.080010e+118001.08.01804UNIVERSIDAD AUTONOMA DEL CARIBE-BARRANQUILLAUNIVERSIDADARQUITECTURAATLANTICO8001BARRANQUILLAARQUITECTURAATLANTICO8001BARRANQUILLAUNIVERSITARIOPRESENCIALEntre 4 millones y menos de 5.5 millonesATLANTICO8001.0BARRANQUILLANoSi01466.0NCOLOMBIAESTUDIANTEFCOL CRISTO REYNoPUBLICAR17/09/1996NoTISecundaria (Bachillerato) completaNoSiEstrato 1SiNoSecundaria (Bachillerato) completaNO OFICIAL - CORPORACIÓN122107.01.0A2124133.01242
920225EK202250071644CCCOLOMBIA8.0ATLANTICO8758.0SOLEDAD3.080010e+118001.08.01804UNIVERSIDAD AUTONOMA DEL CARIBE-BARRANQUILLAUNIVERSIDADARQUITECTURAATLANTICO8001BARRANQUILLAARQUITECTURAATLANTICO8001BARRANQUILLAUNIVERSITARIOPRESENCIALEntre 4 millones y menos de 5.5 millonesATLANTICO8001.0BARRANQUILLANoNo01466.0NCOLOMBIAESTUDIANTEFCOLEGIO MONSEÑOR VICTOR TAMAYO (ASOBOSQUE)SiPUBLICAR22/12/2000NoTITécnica o tecnológica completaSiSiEstrato 2SiSiSecundaria (Bachillerato) completaNO OFICIAL - CORPORACIÓN141149.02.0B1166183.01522